From d583a9aa67c7acbb30e285af957c9d77ab0eab89 Mon Sep 17 00:00:00 2001 From: Anyang Peng <137014849+anyangml@users.noreply.github.com> Date: Tue, 29 Sep 2026 15:17:41 +0800 Subject: [PATCH 1/9] Score homonuclear diatomics on PBE bond length, wall position, and force flips. The previous roughness score was scale-invariant and only covered 14 main-group dimers, so it missed equilibrium geometry and the short-range wall. Co-authored-by: Cursor --- lambench/metrics/utils.py | 108 +- .../metrics/vishelper/metrics_calculations.py | 20 +- lambench/metrics/vishelper/results_fetcher.py | 2 +- .../tasks/calculator/diatomics/diatomics.json | 1603 +---------------- .../tasks/calculator/diatomics/diatomics.py | 483 +++-- tests/metrics/test_metrics_calculator.py | 12 +- tests/tasks/calculator/test_diatomics.py | 278 ++- 7 files changed, 621 insertions(+), 1885 deletions(-) diff --git a/lambench/metrics/utils.py b/lambench/metrics/utils.py index 58d00ec..9e4c862 100644 --- a/lambench/metrics/utils.py +++ b/lambench/metrics/utils.py @@ -1,6 +1,3 @@ -import json -from functools import lru_cache - import numpy as np import yaml from typing import Optional, Literal @@ -10,14 +7,6 @@ from lambench.workflow.entrypoint import gather_model_params, gather_model from datetime import datetime -_DIATOMICS_JSON = ( - Path(__file__).resolve().parent.parent - / "tasks" - / "calculator" - / "diatomics" - / "diatomics.json" -) - ############################# # General utility functions # ############################# @@ -162,41 +151,90 @@ def aggregated_inference_efficiency_results( } -@lru_cache -def _diatomics_molecule_names() -> tuple[str, ...]: - with open(_DIATOMICS_JSON) as fh: - return tuple(entry["name"] for entry in json.load(fh)) +def _empty_diatomics_agg() -> dict[str, float | None]: + return { + "bond_length_mae": None, + "wall_dist_mae": None, + "force_flip_rate": None, + "coverage": 0.0, + "score": None, + } -def _empty_diatomics_agg() -> dict[str, float | None]: - return {"avg_roughness": None} +def _mean_or_none(values: list[float]) -> float | None: + if not values: + return None + return float(np.mean(values)) -def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float]: - """ - Aggregate per-molecule diatomics results. +def _dummy_hat(value: float | None, dummy: float) -> float | None: + if value is None or not np.isfinite(value) or not np.isfinite(dummy) or dummy <= 0: + return None + return float(min(value / dummy, 1.0)) - avg_roughness: mean slope MAE relative to a constant dummy, capped at 1. - Requires a finite roughness for every molecule in diatomics.json; otherwise None. + +def _element_is_excluded(record: dict | None) -> bool: + if not isinstance(record, dict) or record.get("model_excluded", True): + return True + flip = record.get("force_flip_fail") + return flip is None or not np.isfinite(flip) + + +def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float | None]: + """Aggregate per-element diatomic curves into one coverage-weighted score. + + bond_length_mae and wall_dist_mae are raw MAEs in Å. force_flip_rate is + the fraction of scored elements whose Fx changes sign other than once. + Each geometry MAE is divided by the PBE mean-predictor dummy and capped + at 1. score is the equal-weight average of those three terms divided by + coverage. Missing elements and model_excluded curves lower coverage + instead of discarding the model. score is None when coverage is zero or + any of the three terms has no finite samples. """ - if not results: - return _empty_diatomics_agg() + from lambench.tasks.calculator.diatomics.diatomics import ( + reference_dummy_scales, + scored_element_names, + ) - names = _diatomics_molecule_names() + names = scored_element_names() if not names: return _empty_diatomics_agg() - roughness_values = [] + bond_errors: list[float] = [] + wall_errors: list[float] = [] + flip_fails: list[float] = [] + n_excluded = 0 for name in names: - mol_results = results.get(name) - if mol_results is None: - return _empty_diatomics_agg() - roughness = mol_results.get("roughness") - if roughness is None or not np.isfinite(roughness): - return _empty_diatomics_agg() - roughness_values.append(roughness) - - return {"avg_roughness": float(np.mean(roughness_values))} + record = None if not results else results.get(name) + if _element_is_excluded(record): + n_excluded += 1 + continue + bond = record.get("bond_length_error") + wall = record.get("wall_dist_error") + if bond is not None and np.isfinite(bond): + bond_errors.append(float(bond)) + if wall is not None and np.isfinite(wall): + wall_errors.append(float(wall)) + flip_fails.append(float(record["force_flip_fail"])) + + coverage = (len(names) - n_excluded) / len(names) + bond_mae = _mean_or_none(bond_errors) + wall_mae = _mean_or_none(wall_errors) + flip_rate = _mean_or_none(flip_fails) + dummy = reference_dummy_scales() + bond_hat = _dummy_hat(bond_mae, dummy["bond_length_mae"]) + wall_hat = _dummy_hat(wall_mae, dummy["wall_dist_mae"]) + if coverage <= 0 or bond_hat is None or wall_hat is None or flip_rate is None: + score = None + else: + score = float((bond_hat + wall_hat + flip_rate) / (3.0 * coverage)) + return { + "bond_length_mae": bond_mae, + "wall_dist_mae": wall_mae, + "force_flip_rate": flip_rate, + "coverage": float(coverage), + "score": score, + } #################################### diff --git a/lambench/metrics/vishelper/metrics_calculations.py b/lambench/metrics/vishelper/metrics_calculations.py index 938936f..086d0e6 100644 --- a/lambench/metrics/vishelper/metrics_calculations.py +++ b/lambench/metrics/vishelper/metrics_calculations.py @@ -197,17 +197,17 @@ def _calculate_instability_error(self, cell: dict, lambda_0: float = 5e-4) -> fl else: return np.clip(np.log10(slope / lambda_0), a_min=0, a_max=None) - def calculate_diatomics_roughness_results(self) -> dict[str, float]: + def calculate_diatomics_results(self) -> dict[str, float]: """ - Leaderboard scores for homonuclear diatomics: avg_roughness (lower is better). - Models with missing results are omitted from this dict; the final ranking - still includes them with a null roughness column. + Leaderboard scores for homonuclear diatomics: coverage-weighted score + (lower is better). Models with missing results are omitted from this + dict; the final ranking still includes them with a null column. """ raw = self.fetcher.fetch_diatomics_results() return { - model: metrics["avg_roughness"] + model: metrics["score"] for model, metrics in raw.items() - if metrics is not None and metrics.get("avg_roughness") is not None + if metrics is not None and metrics.get("score") is not None } def calculate_efficiency_results(self) -> dict[str, float]: @@ -236,7 +236,7 @@ def summarize_final_rankings(self): ) stability_results = self.calculate_stability_results() efficiency_results = self.calculate_efficiency_results() - roughness_results = self.calculate_diatomics_roughness_results() + diatomics_results = self.calculate_diatomics_results() if not generalizability_ood or not generalizability_downstream: logging.warning( "Missing data for generalizability metrics (ood or downstream)" @@ -270,8 +270,8 @@ def summarize_final_rankings(self): "Applicability-Efficiency ↑": [ efficiency_results[model] for model in shared_models ], - "Applicability-Roughness ↓": [ - roughness_results.get(model) for model in shared_models + "Applicability-Diatomics ↓": [ + diatomics_results.get(model) for model in shared_models ], } @@ -291,7 +291,7 @@ def summarize_final_rankings(self): "Generalizability-PC Error ↓", "Applicability-Instability ↓", "Applicability-Efficiency ↑", - "Applicability-Roughness ↓", + "Applicability-Diatomics ↓", ], ascending=[True, True, True, False, True], ) diff --git a/lambench/metrics/vishelper/results_fetcher.py b/lambench/metrics/vishelper/results_fetcher.py index e6c3131..df83ae9 100644 --- a/lambench/metrics/vishelper/results_fetcher.py +++ b/lambench/metrics/vishelper/results_fetcher.py @@ -129,7 +129,7 @@ def fetch_inference_efficiency_results(self) -> dict[str, dict[str, float]]: return results def fetch_diatomics_results(self) -> dict[str, dict]: - """Returns aggregated diatomics roughness results for all leaderboard models.""" + """Returns aggregated homonuclear-diatomic scores for leaderboard models.""" results = {} for model in self.leaderboard_models: task_results = CalculatorRecord.query( diff --git a/lambench/tasks/calculator/diatomics/diatomics.json b/lambench/tasks/calculator/diatomics/diatomics.json index 1a6e58a..7562d00 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.json +++ b/lambench/tasks/calculator/diatomics/diatomics.json @@ -1,1602 +1 @@ -[ - { - "name": "AlAl", - "method": "PBE", - "R": [ - 1.21, - 1.41589286, - 1.62178572, - 1.82767858, - 2.03357142, - 2.23946428, - 2.44535714, - 2.65125, - 2.85714286, - 3.06303572, - 3.26892858, - 3.47482142, - 3.68071428, - 3.88660714, - 4.0925, - 4.29839286, - 4.50428572, - 4.71017858, - 4.91607142, - 5.12196428, - 5.32785714, - 5.53375, - 5.73964286, - 5.94553572, - 6.15142858, - 6.35732142, - 6.56321428, - 6.76910714, - 6.975 - ], - "E": [ - 21.69553068, - 11.14410695, - 4.46990509, - 0.69626246, - -1.21676381, - -2.04208305, - -2.27886427, - -2.22546192, - -2.23126744, - -2.13493129, - -1.99294153, - -1.83531198, - -1.67788789, - -1.5284843, - -1.39083808, - -1.26651214, - -1.1571301, - -1.05868295, - -0.97419251, - -0.90180437, - -0.83982298, - -0.78814601, - -0.74514801, - -0.71051327, - -0.68320322, - -0.66276761, - -0.64851475, - -0.64011496, - -0.57647315 - ], - "F": [ - 60.57266875, - 41.354763, - 24.44625509, - 13.06806215, - 6.14128837, - 2.26162033, - 0.25579529, - 0.41602807, - -0.27806619, - -0.61013121, - -0.74631893, - -0.7757976, - -0.7518143, - -0.70137733, - -0.64001526, - -0.57416177, - -0.48706032, - -0.44642018, - -0.38713862, - -0.33408605, - -0.27793413, - -0.22865668, - -0.1892725, - -0.15183375, - -0.11960044, - -0.08665681, - -0.0572195, - -0.02949642, - 0.0 - ], - "S^2": [ - 0.106276, - 0.098596, - 0.058564, - 0.038809, - 0.0289, - 0.023409, - 0.020449, - 0.025281, - 0.022801, - 0.020736, - 0.019321, - 0.018225, - 0.017424, - 0.0169, - 0.016641, - 0.016384, - 0.016384, - 0.016129, - 0.016384, - 0.016384, - 0.016641, - 0.0169, - 0.016641, - 0.0169, - 0.0169, - 0.017161, - 0.017161, - 0.017161, - 0.017161 - ] - }, - { - "name": "SiSi", - "method": "PBE", - "R": [ - 1.11, - 1.31282142, - 1.51564286, - 1.71846428, - 1.92128572, - 2.12410714, - 2.32692858, - 2.52975, - 2.73257142, - 2.93539286, - 3.13821428, - 3.34103572, - 3.54385714, - 3.74667858, - 3.9495, - 4.15232142, - 4.35514286, - 4.55796428, - 4.76078572, - 4.96360714, - 5.16642858, - 5.36925, - 5.57207142, - 5.77489286, - 5.97771428, - 6.18053572, - 6.38335714, - 6.58617858, - 6.789 - ], - "E": [ - 33.12026864, - 13.25774131, - 2.00110273, - -2.49417775, - -4.44198323, - -5.1071608, - -5.24703455, - -4.98742036, - -4.54345777, - -4.04252783, - -3.54839397, - -3.0996546, - -2.68388013, - -2.32916398, - -2.38126229, - -2.24245054, - -2.13768097, - -2.03626856, - -1.9573986, - -1.88489854, - -1.84197426, - -1.80103498, - -1.78620606, - -1.74383145, - -1.68289036, - -1.7109778, - -1.70150633, - -1.69604422, - -0.87555372 - ], - "F": [ - 124.45839432, - 68.53195105, - 33.04559539, - 13.37175758, - 6.21281285, - 0.81988881, - -0.47728931, - -1.88020767, - -2.39683204, - -2.48400236, - -2.35926277, - -2.20970491, - -1.87359833, - -1.61397952, - -0.73841286, - -0.62991633, - -0.52491607, - -0.43387721, - -0.35474778, - -0.31693349, - -0.22822001, - -0.1881757, - -0.13374293, - -0.11164342, - -0.04003388, - -0.0597873, - -0.03960887, - -0.02345417, - 0.0 - ], - "S^2": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.130321, - 0.114921, - 0.069696, - 0.067081, - 0.066049, - 0.067081, - 0.068644, - 0.071289, - 0.072361, - 0.074529, - 0.310249, - 0.311364, - 0.314721, - 0.3136, - 0.315844, - 0.315844, - 0.318096, - 0.319225, - 0.319225, - 0.321489, - 0.326041, - 0.321489, - 0.322624, - 0.322624, - 0.341056 - ] - }, - { - "name": "SS", - "method": "PBE", - "R": [ - 1.05, - 1.250375, - 1.45075, - 1.651125, - 1.8515, - 2.051875, - 2.25225, - 2.452625, - 2.653, - 2.853375, - 3.05375, - 3.254125, - 3.4545, - 3.654875, - 3.85525, - 4.055625, - 4.256, - 4.456375, - 4.65675, - 4.857125, - 5.0575, - 5.257875, - 5.45825, - 5.658625, - 5.859 - ], - "E": [ - 56.6632406, - 17.59053523, - -0.48155823, - -5.78096745, - -7.12850504, - -6.90163556, - -6.10408707, - -5.1706445, - -4.26951883, - -3.48454744, - -2.82856388, - -2.61429184, - -2.45644616, - -2.31281962, - -2.30469358, - -2.21163025, - -2.13131514, - -2.07984364, - -2.03543091, - -2.00166027, - -2.14408425, - -2.14443081, - -2.14453532, - -2.14455228, - -1.89473979 - ], - "F": [ - 317.15397377, - 120.14746006, - 43.86271234, - 13.66400583, - 1.60573497, - -3.13060913, - -4.51443905, - -4.61397552, - -4.20957069, - -3.59285054, - -2.95131997, - -0.82030173, - -0.74309327, - -0.58769699, - -0.52709542, - -0.41845518, - 0.03331956, - -0.25494417, - -0.18020652, - -0.14812717, - 0.00442569, - 0.00474803, - 0.00328453, - 0.002413, - 0.0 - ], - "S^2": [ - 0.133225, - 0.221857, - 0.252004, - 0.300304, - 0.334084, - 0.351649, - 0.358801, - 0.358801, - 0.356409, - 0.352836, - 0.346921, - 1.340964, - 1.334025, - 1.279161, - 1.311025, - 1.301881, - 1.238769, - 1.288225, - 1.281424, - 1.2769, - 1.245456, - 1.245456, - 1.245456, - 1.245456, - 1.243225 - ] - }, - { - "name": "LiLi", - "method": "PBE", - "R": [ - 1.28, - 1.48353846, - 1.68707692, - 1.89061538, - 2.09415384, - 2.2976923, - 2.50123076, - 2.70476924, - 2.9083077, - 3.11184616, - 3.31538462, - 3.51892308, - 3.72246154, - 3.926, - 4.12953846, - 4.33307692, - 4.53661538, - 4.74015384, - 4.9436923, - 5.14723076, - 5.35076924, - 5.5543077, - 5.75784616, - 5.96138462, - 6.16492308, - 6.36846154, - 6.572 - ], - "E": [ - 2.57567241, - 1.24409369, - 0.21632582, - -0.51468971, - -0.98958252, - -1.26786043, - -1.40617495, - -1.44929497, - -1.42991395, - -1.37109178, - -1.28881753, - -1.19406826, - -1.09431241, - -0.99457511, - -0.89818493, - -0.80729571, - -0.72325252, - -0.64684731, - -0.57850035, - -0.51839306, - -0.46655827, - -0.42294349, - -0.38745548, - -0.35998967, - -0.3404493, - -0.32875772, - -0.32485202 - ], - "F": [ - 7.27327891, - 5.80165488, - 4.30052111, - 2.91692256, - 1.79992067, - 0.98140216, - 0.41437564, - 0.03606571, - -0.20753374, - -0.35715219, - -0.44197593, - -0.4825392, - -0.49314499, - -0.4837446, - -0.4612383, - -0.430389, - -0.39446085, - -0.35568997, - -0.31551901, - -0.2748906, - -0.23433785, - -0.19417691, - -0.15451309, - -0.11535542, - -0.0766322, - -0.03822277, - 0.0 - ], - "S^2": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ] - }, - { - "name": "BB", - "method": "PBE", - "R": [ - 0.84, - 1.04324, - 1.24648, - 1.44972, - 1.65296, - 1.8562, - 2.05944, - 2.26268, - 2.46592, - 2.66916, - 2.8724, - 3.07564, - 3.27888, - 3.48212, - 3.68536, - 3.8886, - 4.09184, - 4.29508, - 4.49832, - 4.70156, - 4.9048, - 5.10804, - 5.31128, - 5.51452, - 5.71776, - 5.921 - ], - "E": [ - 17.92459699, - 5.10213103, - -1.46814943, - -3.82963048, - -4.20597957, - -3.81948386, - -3.2179762, - -2.91230378, - -2.52699665, - -2.1845205, - 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1.307684, 1.351719, 1.397237, 1.444288, 1.492923, 1.543196, 1.595162, 1.648877, 1.704402, 1.761796, 1.821123, 1.882448, 1.945838, 2.011362, 2.079093, 2.149105, 2.221474, 2.296281, 2.373606, 2.453535, 2.536156, 2.621559, 2.709838, 2.801089, 2.895414, 2.992914, 3.093698, 3.197876, 3.305562, 3.416874, 3.531934, 3.650869, 3.773809, 3.900889, 4.032248, 4.168031, 4.308386, 4.453467, 4.603434, 4.758451, 4.918688, 5.084321, 5.255531, 5.432507, 5.615442, 5.804537, 6.0], "E": [97.035128, 89.340433, 81.713237, 74.159803, 66.709541, 59.403375, 52.283111, 45.385954, 38.746459, 32.405455, 26.42031, 20.867245, 15.829189, 11.374678, 7.540873, 4.328086, 1.704169, -0.386198, -2.011074, -3.24191, -4.146372, -4.783937, -5.204141, -5.447073, -5.545116, -5.52513, -5.410255, -5.221725, -5.003051, -4.776648, -4.548609, -4.323784, -4.106045, -3.898646, -3.704428, -3.525954, -3.365422, -3.224396, -3.103469, -3.002195, -2.919226, -2.852625, -2.800168, -2.75959, -2.72874, -2.705679, -2.688728, -2.676473, -2.667761, -2.661685]}, {"element": "Ac", "method": "PBE", "R": [1.72, 1.764422, 1.809991, 1.856736, 1.904689, 1.953881, 2.004343, 2.056108, 2.109211, 2.163684, 2.219565, 2.276888, 2.335692, 2.396015, 2.457896, 2.521375, 2.586494, 2.653294, 2.721819, 2.792114, 2.864225, 2.938198, 3.014082, 3.091925, 3.171779, 3.253695, 3.337727, 3.423928, 3.512357, 3.603069, 3.696124, 3.791582, 3.889505, 3.989958, 4.093005, 4.198713, 4.307151, 4.41839, 4.532502, 4.64956, 4.769643, 4.892826, 5.019191, 5.148819, 5.281795, 5.418206, 5.55814, 5.701687, 5.848942, 6.0], "E": [42.137138, 35.757884, 30.101166, 25.132503, 20.807614, 17.075118, 13.879298, 11.162847, 8.869433, 6.945824, 5.343217, 3.973831, 2.728238, 1.705791, 0.87618, 0.211672, -0.313154, -0.721342, -1.033107, -1.265306, -1.431179, -1.615057, -1.81691, -1.968465, -2.076392, -2.144594, -2.176634, -2.176065, -2.146462, -2.09829, -2.105362, -2.087975, -2.049883, -1.994699, -1.92587, -1.84657, -1.759709, -1.667881, -1.573393, -1.47824, -1.384096, -1.292403, -1.204319, -1.120742, -1.04237, -0.969695, -0.903046, -0.842599, -0.788395, -0.740244]}, {"element": "Th", "method": "PBE", "R": [1.648, 1.692038, 1.737253, 1.783676, 1.83134, 1.880277, 1.930522, 1.98211, 2.035076, 2.089458, 2.145293, 2.20262, 2.261479, 2.32191, 2.383957, 2.447661, 2.513068, 2.580223, 2.649172, 2.719963, 2.792647, 2.867272, 2.943892, 3.022559, 3.103329, 3.186256, 3.2714, 3.358819, 3.448574, 3.540727, 3.635343, 3.732487, 3.832227, 3.934633, 4.039775, 4.147726, 4.258563, 4.372361, 4.4892, 4.609161, 4.732328, 4.858786, 4.988623, 5.12193, 5.258799, 5.399325, 5.543607, 5.691744, 5.84384, 6.0], "E": [37.492082, 30.777752, 24.644685, 19.083902, 14.295199, 10.210828, 6.766003, 3.895265, 1.53503, -0.375182, -1.892167, -3.068146, -3.969139, -4.814512, -5.393282, -5.790955, -6.040664, -6.171612, -6.206617, -6.178965, -6.157191, -6.07493, -5.943053, -5.831868, -5.742558, -5.62325, -5.479356, -5.315849, -5.136904, -4.945945, -4.745904, -4.539765, -4.330246, -4.117834, -4.052374, -3.862226, -3.674351, -3.490771, -3.313158, -3.142931, -2.981172, -2.828695, -2.715725, -2.629767, -2.551251, -2.452046, -2.415517, -2.364919, -2.3291, -2.293347]}, {"element": "Pa", "method": "PBE", "R": [1.6, 1.643747, 1.68869, 1.734861, 1.782295, 1.831026, 1.88109, 1.932522, 1.98536, 2.039644, 2.095411, 2.152703, 2.211562, 2.27203, 2.334151, 2.397971, 2.463535, 2.530892, 2.600091, 2.671182, 2.744217, 2.819249, 2.896332, 2.975522, 3.056878, 3.140459, 3.226324, 3.314537, 3.405163, 3.498266, 3.593914, 3.692178, 3.793128, 3.896839, 4.003385, 4.112845, 4.225297, 4.340824, 4.459509, 4.58144, 4.706705, 4.835394, 4.967602, 5.103425, 5.242961, 5.386313, 5.533584, 5.684881, 5.840316, 6.0], "E": [29.300953, 22.177128, 15.972577, 10.676506, 6.244261, 2.611331, -0.296117, -2.72515, -4.664464, -6.109299, -7.135071, -7.873599, -8.418405, -8.708065, -8.812576, -8.814362, -8.718727, -8.490722, -8.372064, -8.313261, -8.255189, -8.164251, -8.045984, -7.906668, -7.750573, -7.615898, -7.400543, -7.23625, -7.115917, -6.956101, -6.775775, -6.614926, -6.454341, -6.297301, -6.138399, -6.045402, -5.92855, -5.810593, -5.694284, -5.529324, -5.359805, -5.372853, -5.278409, -5.191304, -5.111936, -5.040494, -4.988958, -5.014203, -4.993124, -4.974246]}, {"element": "U", "method": "PBE", "R": [1.568, 1.611536, 1.656281, 1.702268, 1.749532, 1.798109, 1.848034, 1.899345, 1.952081, 2.006282, 2.061987, 2.119239, 2.17808, 2.238555, 2.30071, 2.36459, 2.430244, 2.49772, 2.56707, 2.638346, 2.711601, 2.786889, 2.864268, 2.943796, 3.025531, 3.109536, 3.195874, 3.284609, 3.375807, 3.469537, 3.56587, 3.664878, 3.766635, 3.871217, 3.978703, 4.089173, 4.20271, 4.3194, 4.43933, 4.56259, 4.689272, 4.819471, 4.953286, 5.090815, 5.232164, 5.377437, 5.526743, 5.680195, 5.837908, 6.0], "E": [26.16394, 18.422049, 11.736865, 6.023865, 1.288481, -2.539143, -5.540092, -7.807331, -9.439973, -10.538813, -11.201971, -11.580226, -11.976865, -12.187778, -12.223834, -12.13293, -11.985716, -11.813709, -11.833163, -11.875776, -11.878231, -11.847671, -11.786741, -11.736431, -11.712511, -11.655744, -11.569857, -11.459325, -11.327502, -11.177882, -11.014081, -10.839916, -10.659247, -10.475741, -10.292667, -10.19601, -10.094126, -10.009331, -10.033879, -9.952288, -9.894087, -9.843289, -9.793471, -9.745188, -9.698835, -9.59743, -9.573326, -9.551553, -9.532262, -9.535962]}] diff --git a/lambench/tasks/calculator/diatomics/diatomics.py b/lambench/tasks/calculator/diatomics/diatomics.py index dda5419..5eae485 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.py +++ b/lambench/tasks/calculator/diatomics/diatomics.py @@ -1,180 +1,387 @@ -""" -The reference data (diatomics.json) is derived from the MLIP Arena project: - - MLIP Arena — Benchmark machine learning interatomic potential at scale - Yuan Chiang, Lawrence Berkeley National Laboratory - https://github.com/atomind-ai/mlip-arena - -Licensed under the Apache License, Version 2.0 (the "License"); you may not -use this file except in compliance with the License. You may obtain a copy of -the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software distributed -under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR -CONDITIONS OF ANY KIND, either express or implied. See the License for the -specific language governing permissions and limitations under the License. - ----- - -Homonuclear diatomics dissociation curves (Applicability). - -Same comparison as stacking_fault, on the DFT bond lengths (no PCHIP): -shift each scan so min(E)=0, then MAE of d(E / max(E)) / dr, divided -by a constant-energy dummy (zeros after the shift) and capped at 1: - - 0 = perfect match, 1 = dummy or worse. - - roughness – slope MAE relative to dummy; leaderboard - -The last point of every scan is dropped (PBE tail artifacts). - -Reference data: lambench/tasks/calculator/diatomics/diatomics.json - name, method, R, E, F, S^2 +"""Homonuclear diatomic curves (Applicability). + +Reference curves are the PBE subset of the Matbench Discovery diatomic +reference (Figshare 68541277): VASP 6, PBE_64 PAW, MP24 static settings, +15 Å box, one spin ladder per element merged by the lowest energy at each +distance. r2SCAN curves from that file are not scored. Po, At, Rn, Fr, and +Ra are left out of the scored set (87 elements, H–U). + +Three per-element metrics are averaged over finite values: + +- bond_length_error: absolute equilibrium-distance error in Å. The distance + is a quadratic fit of up to five points around the minimum. Reference + binding energies below 0.05 eV are skipped, as are PBE curves that fail + the smoothness gate. +- wall_dist_error: MAE in Å of repulsive-branch radii at 1, 5, 10, 20, 50, + and 100 eV above the well. A threshold the reference reaches and the + model does not contributes the full reference radius. +- force_flip_fail: 0 when Fx on the first atom changes sign exactly once + after dropping |Fx| < 0.01 eV/Å, otherwise 1. + +Geometry metrics use distances from 0.9 covalent radii to +min(3.1 Alvarez vdW radii, 6 Å). The wall metric extends the lower bound +to 0.8 covalent radii. A model curve that is non-finite in that range, or +that has an energy jump of at least 1.5 eV and at least three +energy-difference sign flips, is dropped from the averages and lowers +coverage. + +The leaderboard score is the equal-weight average of the dummy-normalized +bond-length MAE, the dummy-normalized wall MAE, and the force-flip rate, +divided by coverage. The dummy predicts each geometric quantity by the +mean of the PBE reference. Zero is a perfect single-well match. + +Reference file: lambench/tasks/calculator/diatomics/diatomics.json """ from __future__ import annotations import json import logging +from functools import lru_cache from pathlib import Path from typing import TYPE_CHECKING import numpy as np from ase import Atoms from ase.calculators.calculator import Calculator +from ase.data import atomic_numbers, covalent_radii, vdw_alvarez if TYPE_CHECKING: from lambench.models.ase_models import ASEModel _LABEL_FILE = Path(__file__).parent / "diatomics.json" -_DROP_LAST_POINTS = 1 - - -def _element_from_name(mol_name: str) -> str: - """Extract element symbol: 'AlAl' → 'Al', 'HH' → 'H'.""" - if len(mol_name) % 2: - raise ValueError(f"Expected a repeated homonuclear label, got {mol_name!r}") - half = mol_name[: len(mol_name) // 2] - if mol_name != half + half: - raise ValueError(f"Expected a repeated homonuclear label, got {mol_name!r}") - return half - - -def _scan_arrays(entry: dict) -> tuple[np.ndarray, np.ndarray]: - """Bond lengths (Å) and DFT energies (eV), with the last point dropped.""" - bond_lengths = np.asarray(entry["R"], dtype=float)[:-_DROP_LAST_POINTS] - dft_energies = np.asarray(entry["E"], dtype=float)[:-_DROP_LAST_POINTS] - if bond_lengths.size != dft_energies.size: - raise ValueError( - f"{entry.get('name', '')}: R and E length mismatch after trim" - ) - return bond_lengths, dft_energies - - -def _shift_to_min(energies: np.ndarray) -> np.ndarray: - return energies - np.min(energies) +_EXCLUDED_ELEMENTS = frozenset({"Po", "At", "Rn", "Fr", "Ra"}) +_WALL_THRESHOLDS_EV = (1.0, 5.0, 10.0, 20.0, 50.0, 100.0) +_MIN_BINDING_EV = 0.05 +_MIN_ENERGY_JUMP_EV = 1.5 +_MIN_ENERGY_FLIPS = 3 +_MIN_WINDOW_POINTS = 5 +_FORCE_SIGN_THRESHOLD = 1e-2 +_ENERGY_DIFF_THRESHOLD = 1e-3 +_FIT_POINTS = 5 +_BOX_ANGSTROM = 30.0 + + +def eval_window( + element: str, seps_max: float, *, r_min_factor: float = 0.9 +) -> tuple[float, float]: + """Element-specific scoring window in Å. + + The default lower bound is 0.9 covalent radii. Wall scoring passes + ``r_min_factor=0.8``. The upper bound is min(3.1 Alvarez vdW radii, + ``seps_max``). Missing vdW radii leave the upper bound at ``seps_max``. + """ + atomic_num = atomic_numbers[element] + r_cov = float(covalent_radii[atomic_num]) + r_min = r_min_factor * r_cov if np.isfinite(r_cov) else 0.0 + r_vdw = float(vdw_alvarez.vdw_radii[atomic_num]) + r_max = min(3.1 * r_vdw, seps_max) if np.isfinite(r_vdw) else float(seps_max) + return r_min, r_max -def _normalized_slopes(bond_lengths: np.ndarray, y: np.ndarray) -> np.ndarray: - """d(E / max(E)) / dr on the native DFT scan (Å⁻¹).""" - peak = float(np.max(y)) - slopes = np.diff(y) / np.diff(bond_lengths) - if peak > 0: - return slopes / peak - return np.zeros_like(slopes) +def _energy_smoothness(energies: np.ndarray) -> tuple[float, int]: + """Energy jump (eV) at sign flips, and the flip count. + Differences smaller than 1e-3 eV are treated as zero, matching the + Matbench Discovery gate. + """ + diffs = np.diff(np.asarray(energies, dtype=float)) + diffs[np.abs(diffs) < _ENERGY_DIFF_THRESHOLD] = 0 + signs = np.sign(diffs) + kept = signs != 0 + diffs = diffs[kept] + signs = signs[kept] + if signs.size < 2: + return 0.0, 0 + flips = np.diff(signs) != 0 + jump = float(np.abs(diffs[:-1][flips]).sum() + np.abs(diffs[1:][flips]).sum()) + return jump, int(np.sum(flips)) + + +def _fails_smoothness_gate(energies: np.ndarray) -> bool: + jump, n_flips = _energy_smoothness(energies) + return jump >= _MIN_ENERGY_JUMP_EV and n_flips >= _MIN_ENERGY_FLIPS + + +def _binding_energy(energies: np.ndarray) -> float: + return float(energies[-1] - np.min(energies)) + + +def _equilibrium_distance(seps: np.ndarray, energies: np.ndarray) -> float: + """Equilibrium separation from a local quadratic fit, in Å.""" + min_idx = int(np.argmin(energies)) + if len(seps) < 3: + return float(seps[min_idx]) + start_idx = min(max(0, min_idx - _FIT_POINTS // 2), max(0, len(seps) - _FIT_POINTS)) + fit_seps = seps[start_idx : start_idx + _FIT_POINTS] + fit_energies = energies[start_idx : start_idx + _FIT_POINTS] + if len(fit_seps) < 3: + return float(seps[min_idx]) + quadratic_coef, linear_coef, _constant = np.polyfit(fit_seps, fit_energies, 2) + if quadratic_coef <= 0: + return float(seps[min_idx]) + equilibrium = -linear_coef / (2 * quadratic_coef) + if fit_seps.min() <= equilibrium <= fit_seps.max(): + return float(equilibrium) + return float(seps[min_idx]) + + +def _repulsive_radius( + seps: np.ndarray, energies: np.ndarray, threshold_ev: float +) -> float: + """Invert the repulsive branch to the radius at E_min + threshold_ev.""" + min_idx = int(np.argmin(energies)) + if min_idx == 0: + return np.nan + radii_inward = seps[min_idx::-1] + energy_above_min = energies[min_idx::-1] - energies[min_idx] + monotonic_energy = np.maximum.accumulate(energy_above_min) + unique_energy, unique_idx = np.unique(monotonic_energy, return_index=True) + if len(unique_energy) < 2 or threshold_ev > unique_energy[-1]: + return np.nan + return float(np.interp(threshold_ev, unique_energy, radii_inward[unique_idx])) + + +def _wall_distance_mae( + ref_seps: np.ndarray, + ref_energies: np.ndarray, + model_seps: np.ndarray, + model_energies: np.ndarray, +) -> float | None: + errors: list[float] = [] + for threshold in _WALL_THRESHOLDS_EV: + radius_ref = _repulsive_radius(ref_seps, ref_energies, threshold) + if not np.isfinite(radius_ref): + continue + radius_pred = _repulsive_radius(model_seps, model_energies, threshold) + errors.append( + abs(radius_pred - radius_ref) if np.isfinite(radius_pred) else radius_ref + ) + if not errors: + return None + return float(np.mean(errors)) + + +def _force_flip_count(force_x: np.ndarray) -> int: + kept = force_x[np.abs(force_x) >= _FORCE_SIGN_THRESHOLD] + if kept.size < 2: + return 0 + return int(np.sum(np.diff(np.sign(kept)) != 0)) + + +def _excluded_record() -> dict[str, float | int | bool | None]: + return { + "bond_length_error": None, + "wall_dist_error": None, + "force_flip_fail": None, + "model_excluded": True, + } + + +def score_curve( + element: str, + distances: np.ndarray, + ref_energies: np.ndarray, + model_energies: np.ndarray, + model_force_x: np.ndarray, + *, + reference_low_quality: bool, +) -> dict[str, float | int | bool | None]: + """Score one homonuclear curve against its PBE reference. + + ``model_excluded`` is true when the model curve cannot be scored. + Reference-quality skips leave ``model_excluded`` false and set the + PBE-relative errors to None. + """ + distances = np.asarray(distances, dtype=float) + ref_energies = np.asarray(ref_energies, dtype=float) + model_energies = np.asarray(model_energies, dtype=float) + model_force_x = np.asarray(model_force_x, dtype=float) + if not ( + distances.size == ref_energies.size == model_energies.size == model_force_x.size + ): + return _excluded_record() + if distances.size < 2 or np.any(np.diff(distances) <= 0): + return _excluded_record() + + seps_max = float(distances[-1]) + r_min, r_max = eval_window(element, seps_max) + wall_r_min = eval_window(element, seps_max, r_min_factor=0.8)[0] - 1e-12 + general = (distances >= r_min) & (distances <= r_max) + wall = (distances >= wall_r_min) & (distances <= r_max) + if int(general.sum()) < _MIN_WINDOW_POINTS or int(wall.sum()) < 2: + return _excluded_record() + if not ( + np.isfinite(model_energies[wall]).all() + and np.isfinite(model_force_x[wall]).all() + ): + return _excluded_record() + if not np.isfinite(ref_energies[wall]).all(): + return _excluded_record() + if _fails_smoothness_gate(model_energies[general]): + return _excluded_record() + + n_flips = _force_flip_count(model_force_x[general]) + result: dict[str, float | int | bool | None] = { + "bond_length_error": None, + "wall_dist_error": None, + "force_flip_fail": 0 if n_flips == 1 else 1, + "model_excluded": False, + } + if reference_low_quality: + return result + + result["wall_dist_error"] = _wall_distance_mae( + distances[wall], + ref_energies[wall], + distances[wall], + model_energies[wall], + ) + ref_general = ref_energies[general] + if _binding_energy(ref_general) >= _MIN_BINDING_EV: + ref_distance = _equilibrium_distance(distances[general], ref_general) + model_distance = _equilibrium_distance( + distances[general], model_energies[general] + ) + result["bond_length_error"] = float(abs(model_distance - ref_distance)) + return result -def _mae(left: np.ndarray, right: np.ndarray) -> float: - return float(np.mean(np.abs(left - right))) +@lru_cache +def load_reference( + path: str | None = None, +) -> dict[str, tuple[np.ndarray, np.ndarray]]: + """Scored PBE curves keyed by element, distances then energies in Å and eV.""" + label_path = _LABEL_FILE if path is None else Path(path) + with open(label_path) as fh: + raw: list[dict] = json.load(fh) + curves: dict[str, tuple[np.ndarray, np.ndarray]] = {} + for entry in raw: + element = entry["element"] + if element in _EXCLUDED_ELEMENTS: + continue + curves[element] = ( + np.asarray(entry["R"], dtype=float), + np.asarray(entry["E"], dtype=float), + ) + return curves -def _ratio_capped_at_dummy(value: float, dummy: float) -> float | None: - if dummy <= 0 or not np.isfinite(dummy) or not np.isfinite(value): - return None - return float(min(value / dummy, 1.0)) +def scored_element_names(path: str | None = None) -> tuple[str, ...]: + return tuple(load_reference(path)) -def _curve_metrics( - bond_lengths: np.ndarray, model_energies: np.ndarray, dft_energies: np.ndarray -) -> dict[str, float] | None: - if not np.all(np.isfinite(model_energies)) or not np.all(np.isfinite(dft_energies)): - return None - if bond_lengths.size < 2: - return None - if ( - bond_lengths.size != model_energies.size - or bond_lengths.size != dft_energies.size - ): - return None - if np.any(np.diff(bond_lengths) == 0): - return None - y_dft = _shift_to_min(dft_energies) - y_model = _shift_to_min(model_energies) - y_dummy = np.zeros_like(y_dft) - - roughness = _ratio_capped_at_dummy( - _mae( - _normalized_slopes(bond_lengths, y_model), - _normalized_slopes(bond_lengths, y_dft), - ), - _mae( - _normalized_slopes(bond_lengths, y_dummy), - _normalized_slopes(bond_lengths, y_dft), - ), - ) - if roughness is None: - return None - return {"roughness": roughness} +@lru_cache +def low_quality_elements(path: str | None = None) -> frozenset[str]: + """PBE curves too jumpy to use as a geometry reference.""" + flagged: set[str] = set() + for element, (distances, energies) in load_reference(path).items(): + r_min, r_max = eval_window(element, float(np.max(distances))) + mask = (distances >= r_min) & (distances <= r_max) + if int(mask.sum()) < _MIN_WINDOW_POINTS: + continue + window = energies[mask] + if not np.isfinite(window).all() or _fails_smoothness_gate(window): + flagged.add(element) + return frozenset(flagged) -def _predict_energies( - calc: Calculator, element: str, bond_lengths: np.ndarray -) -> np.ndarray: - """Evaluate model energy for a homonuclear dimer at each bond length (eV). +@lru_cache +def reference_dummy_scales(path: str | None = None) -> dict[str, float]: + """MAE of predicting each reference geometry by its cross-element mean. - Isolated dimer in a 30 Å cubic cell with PBC, matching the MLIP Arena setup. + Bond lengths use elements that pass the smoothness gate and bind by at + least 0.05 eV. Wall radii use every gated element that reaches a + threshold; the dummy error at each threshold is the deviation from the + mean radius at that threshold. """ - cell = 30.0 - energies = [] - for r in bond_lengths: + low_quality = low_quality_elements(path) + bond_lengths: list[float] = [] + radii: dict[float, dict[str, float]] = {t: {} for t in _WALL_THRESHOLDS_EV} + for element, (distances, energies) in load_reference(path).items(): + if element in low_quality: + continue + seps_max = float(np.max(distances)) + r_min, r_max = eval_window(element, seps_max) + general = (distances >= r_min) & (distances <= r_max) + if ( + int(general.sum()) >= 3 + and _binding_energy(energies[general]) >= _MIN_BINDING_EV + ): + bond_lengths.append( + _equilibrium_distance(distances[general], energies[general]) + ) + wall_r_min = eval_window(element, seps_max, r_min_factor=0.8)[0] - 1e-12 + wall = (distances >= wall_r_min) & (distances <= r_max) + if int(wall.sum()) < 2: + continue + for threshold in _WALL_THRESHOLDS_EV: + radius = _repulsive_radius(distances[wall], energies[wall], threshold) + if np.isfinite(radius): + radii[threshold][element] = radius + + bond = np.asarray(bond_lengths, dtype=float) + bond_dummy = float(np.mean(np.abs(bond - np.mean(bond)))) + per_element: dict[str, list[float]] = {} + for threshold, values in radii.items(): + if not values: + continue + mean_radius = float(np.mean(list(values.values()))) + for element, radius in values.items(): + per_element.setdefault(element, []).append(abs(radius - mean_radius)) + wall_errors = [float(np.mean(errs)) for errs in per_element.values() if errs] + return { + "bond_length_mae": bond_dummy, + "wall_dist_mae": float(np.mean(wall_errors)), + } + + +def _predict_curve( + calc: Calculator, element: str, distances: np.ndarray +) -> tuple[np.ndarray, np.ndarray]: + """Model energy (eV) and Fx on the first atom (eV/Å) at each distance.""" + energies = np.empty(distances.size, dtype=float) + force_x = np.empty(distances.size, dtype=float) + for i, distance in enumerate(distances): atoms = Atoms( symbols=[element, element], - positions=[[0.0, 0.0, 0.0], [r, 0.0, 0.0]], - cell=[cell, cell, cell], + positions=[[0.0, 0.0, 0.0], [float(distance), 0.0, 0.0]], + cell=[_BOX_ANGSTROM, _BOX_ANGSTROM, _BOX_ANGSTROM], pbc=True, ) atoms.calc = calc try: - e = atoms.get_potential_energy() - if not np.isfinite(e): - raise ValueError("non-finite energy") + energy = float(atoms.get_potential_energy()) + forces = np.asarray(atoms.get_forces(), dtype=float) + fx = float(forces[0, 0]) + if not np.isfinite(energy) or not np.isfinite(forces).all(): + raise ValueError("non-finite energy or forces") except Exception as exc: - logging.warning(f"{element}2 @ r={r:.3f} Å failed: {exc}") - e = np.nan - energies.append(e) - return np.array(energies) + logging.warning(f"{element}2 @ r={float(distance):.3f} Å failed: {exc}") + energy = np.nan + fx = np.nan + energies[i] = energy + force_x[i] = fx + return energies, force_x def run_inference(model: ASEModel, test_data: Path | None = None) -> dict[str, dict]: - """Compare model and PBE dissociation curves with the stacking_fault metric.""" - label_path = _LABEL_FILE if test_data is None else test_data / "diatomics.json" - - with open(label_path) as fh: - reference_data: list[dict] = json.load(fh) - - results: dict[str, dict] = {} + """Score model curves on the PBE homonuclear reference grid.""" + label_path = None if test_data is None else str(test_data / "diatomics.json") + reference = load_reference(label_path) + low_quality = low_quality_elements(label_path) calc = model.calc - - for entry in reference_data: - mol_name: str = entry["name"] - bond_lengths, dft_energies = _scan_arrays(entry) - element = _element_from_name(mol_name) - model_energies = _predict_energies(calc, element, bond_lengths) - mol_result = _curve_metrics(bond_lengths, model_energies, dft_energies) - results[mol_name] = mol_result - logging.info(f"{mol_name}: {mol_result}") - + results: dict[str, dict] = {} + for element, (distances, ref_energies) in reference.items(): + model_energies, model_force_x = _predict_curve(calc, element, distances) + element_result = score_curve( + element, + distances, + ref_energies, + model_energies, + model_force_x, + reference_low_quality=element in low_quality, + ) + results[element] = element_result + logging.info(f"{element}: {element_result}") return results diff --git a/tests/metrics/test_metrics_calculator.py b/tests/metrics/test_metrics_calculator.py index 9e2bfb8..d42de25 100644 --- a/tests/metrics/test_metrics_calculator.py +++ b/tests/metrics/test_metrics_calculator.py @@ -90,7 +90,7 @@ def test_summarize_final_rankings(metrics_calculator): metrics_calculator.calculate_stability_results = MagicMock( return_value={"model1": 0.2, "model2": 0.5} ) - metrics_calculator.calculate_diatomics_roughness_results = MagicMock( + metrics_calculator.calculate_diatomics_results = MagicMock( return_value={"model1": 0.05, "model2": 0.03} ) _, result = metrics_calculator.summarize_final_rankings() @@ -99,14 +99,14 @@ def test_summarize_final_rankings(metrics_calculator): assert result.iloc[1]["Model"] == "model1" -def test_calculate_diatomics_roughness_results(metrics_calculator, mock_raw_results): +def test_calculate_diatomics_results(metrics_calculator, mock_raw_results): mock_raw_results.fetch_diatomics_results.return_value = { - "model1": {"avg_roughness": 0.05}, - "model2": {"avg_roughness": 0.03}, + "model1": {"score": 0.05}, + "model2": {"score": 0.03}, "model3": None, - "model4": {"avg_roughness": None}, + "model4": {"score": None}, } - result = metrics_calculator.calculate_diatomics_roughness_results() + result = metrics_calculator.calculate_diatomics_results() assert set(result.keys()) == {"model1", "model2"} np.testing.assert_almost_equal(result["model1"], 0.05) np.testing.assert_almost_equal(result["model2"], 0.03) diff --git a/tests/tasks/calculator/test_diatomics.py b/tests/tasks/calculator/test_diatomics.py index fb97684..7747c18 100644 --- a/tests/tasks/calculator/test_diatomics.py +++ b/tests/tasks/calculator/test_diatomics.py @@ -1,124 +1,216 @@ import numpy as np import pytest +from ase.data import atomic_numbers, covalent_radii +from lambench.metrics.utils import aggregated_diatomics_results from lambench.tasks.calculator.diatomics.diatomics import ( - _curve_metrics, - _element_from_name, - _scan_arrays, + eval_window, + load_reference, + low_quality_elements, + reference_dummy_scales, + score_curve, + scored_element_names, ) -from lambench.metrics.utils import ( - _diatomics_molecule_names, - aggregated_diatomics_results, -) - -def _full_results(**overrides: dict | None) -> dict: - results = {name: {"roughness": 0.02} for name in _diatomics_molecule_names()} - results.update(overrides) - return results +def _parabola(element: str = "Si", shift: float = 0.0, scale: float = 80.0): + r_lo, r_hi = eval_window(element, 6.0, r_min_factor=0.8) + distances = np.linspace(r_lo + 1e-4, r_hi - 1e-4, 60) + r0 = distances[int(len(distances) * 0.65)] + energies = scale * (distances - (r0 + shift)) ** 2 + force_x = distances - (r0 + shift) + return element, distances, r0, energies, force_x -def _well_scan(): - r = np.linspace(0.8, 4.0, 25) - e = (r - 1.4) ** 2 - 1.0 - return r, e - - -def test_scan_arrays_drops_last_point(): - r, e = _scan_arrays( - {"name": "SiSi", "R": [1.0, 1.2, 1.4, 1.6], "E": [0.0, -1.0, -0.5, 10.0]} - ) - np.testing.assert_array_equal(r, [1.0, 1.2, 1.4]) - np.testing.assert_array_equal(e, [0.0, -1.0, -0.5]) +def _record(**overrides): + record = { + "bond_length_error": 0.0, + "wall_dist_error": 0.0, + "force_flip_fail": 0, + "model_excluded": False, + } + record.update(overrides) + return record -def test_perfect_match_is_zero(): - r, e = _well_scan() - metrics = _curve_metrics(r, e, e) - assert metrics is not None - assert metrics["roughness"] == pytest.approx(0.0) - -def test_constant_offset_is_removed_by_min_shift(): - r, e = _well_scan() - metrics = _curve_metrics(r, e + 1.5, e) - assert metrics is not None - assert metrics["roughness"] == pytest.approx(0.0, abs=1e-10) - - -def test_constant_dummy_scores_one(): - r, e = _well_scan() - dummy = _curve_metrics(r, np.full_like(e, e[-1]), e) - match = _curve_metrics(r, e, e) - assert dummy is not None and match is not None - assert dummy["roughness"] == pytest.approx(1.0) - assert dummy["roughness"] > match["roughness"] +def _full_results(**overrides): + results = {name: _record() for name in scored_element_names()} + results.update(overrides) + return results -def test_worse_than_dummy_is_capped_at_one(): - r, e = _well_scan() - y = e - np.min(e) - inverted = _curve_metrics(r, np.max(y) - y, e) - assert inverted is not None - assert inverted["roughness"] == pytest.approx(1.0) +def test_scored_set_and_reference_gate(): + names = scored_element_names() + assert len(names) == 87 + assert names[0] == "H" + assert names[-1] == "U" + for element in ("Po", "At", "Rn", "Fr", "Ra"): + assert element not in names + assert low_quality_elements() == frozenset( + {"Pr", "Pm", "Sm", "Tb", "Dy", "Ho", "Er", "Tm"} + ) + distances, energies = load_reference()["H"] + assert distances[0] == pytest.approx( + 0.8 * float(covalent_radii[atomic_numbers["H"]]) + ) + assert distances[-1] == pytest.approx(6.0) + assert len(distances) == len(energies) -def test_zero_bond_step_returns_none(): - r = np.array([1.0, 1.0, 1.4]) - e = np.array([0.0, -1.0, -0.5]) - assert _curve_metrics(r, e, e) is None +def test_dummy_scales_are_positive(): + dummy = reference_dummy_scales() + assert dummy["bond_length_mae"] == pytest.approx(0.6068, abs=1e-3) + assert dummy["wall_dist_mae"] == pytest.approx(0.2733, abs=1e-3) -def test_shape_match_is_scale_invariant(): - r, e = _well_scan() - scaled = _curve_metrics(r, 10 * e, e) - assert scaled is not None - assert scaled["roughness"] == pytest.approx(0.0, abs=1e-10) +def test_identical_parabola_has_zero_geometry_error(): + element, distances, _r0, energies, force_x = _parabola() + metrics = score_curve( + element, + distances, + energies, + energies, + force_x, + reference_low_quality=False, + ) + assert metrics["model_excluded"] is False + assert metrics["bond_length_error"] == pytest.approx(0.0, abs=1e-6) + assert metrics["wall_dist_error"] == pytest.approx(0.0, abs=1e-6) + assert metrics["force_flip_fail"] == 0 + + +def test_shifted_well_moves_bond_length(): + element, distances, _r0, energies, force_x = _parabola() + _element, _distances, _shifted_r0, shifted, shifted_fx = _parabola(shift=0.15) + metrics = score_curve( + element, + distances, + energies, + shifted, + shifted_fx, + reference_low_quality=False, + ) + assert metrics["bond_length_error"] == pytest.approx(0.15, abs=1e-6) -def test_oscillation_increases_roughness(): - r, e = _well_scan() - match = _curve_metrics(r, e, e) - wiggly = _curve_metrics(r, e + 0.2 * np.sin(25 * (r - r[0])), e) - assert match is not None and wiggly is not None - assert wiggly["roughness"] > match["roughness"] +def test_unbound_reference_skips_bond_length(): + element, distances, r0, _energies, force_x = _parabola(scale=1e-4) + energies = 1e-4 * (distances - r0) ** 2 + metrics = score_curve( + element, distances, energies, energies, force_x, reference_low_quality=False + ) + assert metrics["model_excluded"] is False + assert metrics["bond_length_error"] is None -def test_nonfinite_returns_none(): - r = np.array([1.0, 1.2, 1.4, 1.6]) - e = np.array([0.0, np.nan, -0.5, -0.4]) - assert _curve_metrics(r, e, e) is None +def test_missing_model_wall_uses_reference_radius(): + element, distances, _r0, energies, force_x = _parabola() + flat = np.full_like(energies, energies.min()) + metrics = score_curve( + element, distances, energies, flat, force_x, reference_low_quality=False + ) + assert metrics["wall_dist_error"] is not None + assert metrics["wall_dist_error"] > 0.5 + + +def test_force_sign_must_change_once(): + element, distances, _r0, energies, force_x = _parabola() + oscillating = np.sin(np.linspace(0, 6 * np.pi, len(distances))) + flat = np.zeros_like(force_x) + extra = score_curve( + element, + distances, + energies, + energies, + oscillating, + reference_low_quality=False, + ) + none = score_curve( + element, distances, energies, energies, flat, reference_low_quality=False + ) + assert extra["force_flip_fail"] == 1 + assert none["force_flip_fail"] == 1 + assert extra["model_excluded"] is False -def test_element_from_name(): - assert _element_from_name("HH") == "H" - assert _element_from_name("AlAl") == "Al" +def test_low_quality_reference_skips_geometry(): + element, distances, _r0, energies, force_x = _parabola() + metrics = score_curve( + element, distances, energies, energies, force_x, reference_low_quality=True + ) + assert metrics["model_excluded"] is False + assert metrics["bond_length_error"] is None + assert metrics["wall_dist_error"] is None + assert metrics["force_flip_fail"] == 0 + + +def test_nonfinite_and_jumpy_curves_are_excluded(): + element, distances, _r0, energies, force_x = _parabola() + broken = energies.copy() + broken[len(broken) // 2] = np.nan + assert score_curve( + element, distances, energies, broken, force_x, reference_low_quality=False + )["model_excluded"] + + jumpy = np.zeros_like(energies) + jumpy[10:15] += 5.0 + jumpy[25:30] -= 5.0 + jumpy[40:45] += 5.0 + assert score_curve( + element, distances, energies, jumpy, force_x, reference_low_quality=False + )["model_excluded"] + + +def test_aggregated_perfect_score_is_zero(): + agg = aggregated_diatomics_results(_full_results()) + assert agg["coverage"] == pytest.approx(1.0) + assert agg["bond_length_mae"] == pytest.approx(0.0) + assert agg["wall_dist_mae"] == pytest.approx(0.0) + assert agg["force_flip_rate"] == pytest.approx(0.0) + assert agg["score"] == pytest.approx(0.0) + + +def test_aggregated_caps_geometry_at_dummy(): + dummy = reference_dummy_scales() + agg = aggregated_diatomics_results( + _full_results( + **{ + name: _record(bond_length_error=dummy["bond_length_mae"] * 5) + for name in scored_element_names() + } + ) + ) + assert agg["score"] == pytest.approx(1.0 / 3.0) -def test_element_from_name_rejects_invalid(): - with pytest.raises(ValueError): - _element_from_name("H2") - with pytest.raises(ValueError): - _element_from_name("AlH") +def test_aggregated_flip_rate_and_coverage(): + names = scored_element_names() + results = _full_results(**{name: _record(force_flip_fail=1) for name in names}) + results["H"] = _record(model_excluded=True, force_flip_fail=None) + agg = aggregated_diatomics_results(results) + coverage = (len(names) - 1) / len(names) + assert agg["coverage"] == pytest.approx(coverage) + assert agg["force_flip_rate"] == pytest.approx(1.0) + assert agg["score"] == pytest.approx(1.0 / (3.0 * coverage)) -def test_aggregated_means(): - names = _diatomics_molecule_names() - results = {name: {"roughness": 0.02} for name in names} - results["HH"] = {"roughness": 0.01} - results["NN"] = {"roughness": 0.03} - n = len(names) +def test_aggregated_missing_element_counts_against_coverage(): + results = _full_results() + del results["H"] agg = aggregated_diatomics_results(results) - assert agg["avg_roughness"] == pytest.approx((0.01 + 0.03 + 0.02 * (n - 2)) / n) - + assert agg["coverage"] == pytest.approx((len(results)) / (len(results) + 1)) + assert agg["score"] == pytest.approx(0.0) -def test_aggregated_empty_results(): - agg = aggregated_diatomics_results({}) - assert agg["avg_roughness"] is None +def test_aggregated_empty_or_incomplete_terms_have_no_score(): + empty = aggregated_diatomics_results({}) + assert empty["coverage"] == pytest.approx(0.0) + assert empty["score"] is None -def test_aggregated_incomplete_coverage_is_none(): - assert aggregated_diatomics_results(_full_results(HH=None))["avg_roughness"] is None - incomplete = _full_results() - del incomplete["HH"] - assert aggregated_diatomics_results(incomplete)["avg_roughness"] is None + no_bond = aggregated_diatomics_results( + _full_results( + **{name: _record(bond_length_error=None) for name in scored_element_names()} + ) + ) + assert no_bond["force_flip_rate"] == pytest.approx(0.0) + assert no_bond["score"] is None From 4404e8dce84d3cf493c9bddd356beaa56635ed11 Mon Sep 17 00:00:00 2001 From: Anyang Peng <137014849+anyangml@users.noreply.github.com> Date: Tue, 29 Sep 2026 15:32:50 +0800 Subject: [PATCH 2/9] Store the PBE diatomic curves as a readable JSON file. The scored reference was already committed, but as one 91 KB line, so the pull request diff looked like the old curves had been deleted. Co-authored-by: Cursor --- .../tasks/calculator/diatomics/diatomics.json | 9309 ++++++++++++++++- 1 file changed, 9308 insertions(+), 1 deletion(-) diff --git a/lambench/tasks/calculator/diatomics/diatomics.json b/lambench/tasks/calculator/diatomics/diatomics.json index 7562d00..3d39c3a 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.json +++ b/lambench/tasks/calculator/diatomics/diatomics.json @@ -1 +1,9308 @@ -[{"element": "H", "method": "PBE", "R": [0.248, 0.264661, 0.282442, 0.301417, 0.321667, 0.343278, 0.36634, 0.390952, 0.417217, 0.445247, 0.475159, 0.507082, 0.541149, 0.577505, 0.616303, 0.657708, 0.701895, 0.74905, 0.799373, 0.853077, 0.910389, 0.971551, 1.036823, 1.106479, 1.180816, 1.260146, 1.344806, 1.435154, 1.531571, 1.634466, 1.744274, 1.861459, 1.986517, 2.119976, 2.262402, 2.414396, 2.576602, 2.749705, 2.934437, 3.131581, 3.341968, 3.566491, 3.806097, 4.061801, 4.334684, 4.625899, 4.93668, 5.268339, 5.62228, 6.0], "E": [15.685922, 12.562721, 9.724934, 7.161072, 4.859984, 2.810604, 1.001662, -0.578547, -1.942303, -3.102676, -4.07347, -4.869078, -5.504287, -5.993971, -6.352582, -6.593893, -6.730666, -6.774525, -6.736048, -6.624869, -6.450027, -6.220104, -5.9434, -5.628008, -5.281787, -4.912302, -4.526777, -4.13209, -3.73476, -3.340951, -3.016139, -2.771404, -2.592861, -2.467209, -2.38165, -2.325068, -2.288626, -2.2658, -2.251962, -2.243925, -2.239386, -2.237072, -2.235996, -2.236349, -2.236238, -2.235948, -2.235634, -2.235372, -2.23495, -2.235079]}, {"element": "He", "method": "PBE", "R": [0.224, 0.239546, 0.256171, 0.27395, 0.292962, 0.313294, 0.335037, 0.35829, 0.383156, 0.409747, 0.438184, 0.468595, 0.501116, 0.535895, 0.573087, 0.61286, 0.655394, 0.700879, 0.749521, 0.801539, 0.857167, 0.916656, 0.980274, 1.048307, 1.121061, 1.198864, 1.282068, 1.371045, 1.466198, 1.567955, 1.676774, 1.793145, 1.917592, 2.050676, 2.192997, 2.345195, 2.507955, 2.682012, 2.868148, 3.067202, 3.280071, 3.507714, 3.751156, 4.011492, 4.289897, 4.587623, 4.906012, 5.246498, 5.610614, 6.0], "E": [134.953486, 120.221999, 106.66754, 94.284863, 83.061023, 72.938179, 63.857752, 55.758468, 48.574896, 42.236522, 36.666943, 31.784861, 27.505949, 23.74656, 20.42797, 17.481335, 14.851036, 12.496359, 10.39059, 8.518903, 6.873714, 5.449505, 4.239067, 3.230895, 2.40892, 1.753592, 1.243407, 0.856343, 0.570853, 0.366583, 0.225209, 0.130895, 0.070604, 0.033993, 0.013142, 0.00231, -0.002567, -0.004181, -0.004141, -0.003467, -0.002686, -0.002054, -0.001612, -0.00134, -0.001184, -0.001107, -0.001071, -0.001059, -0.001056, -0.001045]}, {"element": "Li", "method": "PBE", "R": [1.024, 1.061623, 1.100629, 1.141067, 1.182992, 1.226456, 1.271518, 1.318235, 1.366669, 1.416883, 1.468941, 1.522912, 1.578866, 1.636875, 1.697016, 1.759367, 1.824009, 1.891025, 1.960504, 2.032536, 2.107214, 2.184636, 2.264903, 2.348118, 2.434391, 2.523834, 2.616563, 2.7127, 2.812368, 2.915698, 3.022825, 3.133888, 3.249031, 3.368405, 3.492165, 3.620472, 3.753493, 3.891401, 4.034377, 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5.84384, + 6.0 + ], + "E": [ + 37.492082, + 30.777752, + 24.644685, + 19.083902, + 14.295199, + 10.210828, + 6.766003, + 3.895265, + 1.53503, + -0.375182, + -1.892167, + -3.068146, + -3.969139, + -4.814512, + -5.393282, + -5.790955, + -6.040664, + -6.171612, + -6.206617, + -6.178965, + -6.157191, + -6.07493, + -5.943053, + -5.831868, + -5.742558, + -5.62325, + -5.479356, + -5.315849, + -5.136904, + -4.945945, + -4.745904, + -4.539765, + -4.330246, + -4.117834, + -4.052374, + -3.862226, + -3.674351, + -3.490771, + -3.313158, + -3.142931, + -2.981172, + -2.828695, + -2.715725, + -2.629767, + -2.551251, + -2.452046, + -2.415517, + -2.364919, + -2.3291, + -2.293347 + ] + }, + { + "element": "Pa", + "method": "PBE", + "R": [ + 1.6, + 1.643747, + 1.68869, + 1.734861, + 1.782295, + 1.831026, + 1.88109, + 1.932522, + 1.98536, + 2.039644, + 2.095411, + 2.152703, + 2.211562, + 2.27203, + 2.334151, + 2.397971, + 2.463535, + 2.530892, + 2.600091, + 2.671182, + 2.744217, + 2.819249, + 2.896332, + 2.975522, + 3.056878, + 3.140459, + 3.226324, + 3.314537, + 3.405163, + 3.498266, + 3.593914, + 3.692178, + 3.793128, + 3.896839, + 4.003385, + 4.112845, + 4.225297, + 4.340824, + 4.459509, + 4.58144, + 4.706705, + 4.835394, + 4.967602, + 5.103425, + 5.242961, + 5.386313, + 5.533584, + 5.684881, + 5.840316, + 6.0 + ], + "E": [ + 29.300953, + 22.177128, + 15.972577, + 10.676506, + 6.244261, + 2.611331, + -0.296117, + -2.72515, + -4.664464, + -6.109299, + -7.135071, + -7.873599, + -8.418405, + -8.708065, + -8.812576, + -8.814362, + -8.718727, + -8.490722, + -8.372064, + -8.313261, + -8.255189, + -8.164251, + -8.045984, + -7.906668, + -7.750573, + -7.615898, + -7.400543, + -7.23625, + -7.115917, + -6.956101, + -6.775775, + -6.614926, + -6.454341, + -6.297301, + -6.138399, + -6.045402, + -5.92855, + -5.810593, + -5.694284, + -5.529324, + -5.359805, + -5.372853, + -5.278409, + -5.191304, + -5.111936, + -5.040494, + -4.988958, + -5.014203, + -4.993124, + -4.974246 + ] + }, + { + "element": "U", + "method": "PBE", + "R": [ + 1.568, + 1.611536, + 1.656281, + 1.702268, + 1.749532, + 1.798109, + 1.848034, + 1.899345, + 1.952081, + 2.006282, + 2.061987, + 2.119239, + 2.17808, + 2.238555, + 2.30071, + 2.36459, + 2.430244, + 2.49772, + 2.56707, + 2.638346, + 2.711601, + 2.786889, + 2.864268, + 2.943796, + 3.025531, + 3.109536, + 3.195874, + 3.284609, + 3.375807, + 3.469537, + 3.56587, + 3.664878, + 3.766635, + 3.871217, + 3.978703, + 4.089173, + 4.20271, + 4.3194, + 4.43933, + 4.56259, + 4.689272, + 4.819471, + 4.953286, + 5.090815, + 5.232164, + 5.377437, + 5.526743, + 5.680195, + 5.837908, + 6.0 + ], + "E": [ + 26.16394, + 18.422049, + 11.736865, + 6.023865, + 1.288481, + -2.539143, + -5.540092, + -7.807331, + -9.439973, + -10.538813, + -11.201971, + -11.580226, + -11.976865, + -12.187778, + -12.223834, + -12.13293, + -11.985716, + -11.813709, + -11.833163, + -11.875776, + -11.878231, + -11.847671, + -11.786741, + -11.736431, + -11.712511, + -11.655744, + -11.569857, + -11.459325, + -11.327502, + -11.177882, + -11.014081, + -10.839916, + -10.659247, + -10.475741, + -10.292667, + -10.19601, + -10.094126, + -10.009331, + -10.033879, + -9.952288, + -9.894087, + -9.843289, + -9.793471, + -9.745188, + -9.698835, + -9.59743, + -9.573326, + -9.551553, + -9.532262, + -9.535962 + ] + } +] From 6a7e241ba608297616e00e5ac5253d535d9b4a91 Mon Sep 17 00:00:00 2001 From: Anyang Peng <137014849+anyangml@users.noreply.github.com> Date: Tue, 29 Sep 2026 15:40:43 +0800 Subject: [PATCH 3/9] Cite the Matbench Discovery PBE curves and check their shape. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Each of the 87 stored curves runs from 0.8 covalent radii to 6 Å with a short-range wall, and bound curves that pass the smoothness gate have an interior minimum. Co-authored-by: Cursor --- .../tasks/calculator/diatomics/diatomics.py | 28 ++++++++++++++---- tests/tasks/calculator/test_diatomics.py | 29 +++++++++++++++++++ 2 files changed, 52 insertions(+), 5 deletions(-) diff --git a/lambench/tasks/calculator/diatomics/diatomics.py b/lambench/tasks/calculator/diatomics/diatomics.py index 5eae485..99b4aaf 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.py +++ b/lambench/tasks/calculator/diatomics/diatomics.py @@ -1,10 +1,28 @@ """Homonuclear diatomic curves (Applicability). -Reference curves are the PBE subset of the Matbench Discovery diatomic -reference (Figshare 68541277): VASP 6, PBE_64 PAW, MP24 static settings, -15 Å box, one spin ladder per element merged by the lowest energy at each -distance. r2SCAN curves from that file are not scored. Po, At, Rn, Fr, and -Ra are left out of the scored set (87 elements, H–U). +Data source +----------- +PBE curves for homonuclear dimers, from the Matbench Discovery diatomic +reference. The published file also contains r2SCAN; this task does not +score it. + + Matbench Discovery diatomic DFT curves (PBE and r2SCAN, H–U) + https://figshare.com/files/68541277 + + Provenance, VASP settings, and spin ladder + https://github.com/janosh/matbench-discovery/blob/main/site/src/lib/diatomics-dft.readme.md + + Riebesell, J., Goodall, R. E. A., Benner, P. et al. A framework to + evaluate machine learning crystal stability predictions. + Nat. Mach. Intell. 7, 836–847 (2025). + https://doi.org/10.1038/s42256-025-01055-1 + +Each curve was computed with VASP 6, PBE_64 PAW potentials, and Materials +Project MP24 static settings in a 15 Å cell. Distances run geometrically +from 0.8 covalent radii to 6 Å. At each distance the stored energy is the +lowest among an even-NUPDOWN spin ladder plus one antiferromagnetic +candidate. The file checked in here keeps the PBE distances and energies +for H–U except Po, At, Rn, Fr, and Ra (87 elements). Three per-element metrics are averaged over finite values: diff --git a/tests/tasks/calculator/test_diatomics.py b/tests/tasks/calculator/test_diatomics.py index 7747c18..6828562 100644 --- a/tests/tasks/calculator/test_diatomics.py +++ b/tests/tasks/calculator/test_diatomics.py @@ -57,6 +57,35 @@ def test_scored_set_and_reference_gate(): assert len(distances) == len(energies) +def test_reference_curves_have_a_wall_and_an_interior_minimum(): + """Every stored PBE curve is a rising short-range wall on a 0.8 r_cov–6 Å grid. + + Elements that bind by at least 0.05 eV, and that pass the smoothness + gate, also place that minimum inside the scoring window rather than on + an endpoint. + """ + low_quality = low_quality_elements() + for element, (distances, energies) in load_reference().items(): + rcov = float(covalent_radii[atomic_numbers[element]]) + assert np.all(np.diff(distances) > 0) + assert np.isfinite(distances).all() + assert np.isfinite(energies).all() + assert distances[0] == pytest.approx(0.8 * rcov) + assert distances[-1] == pytest.approx(6.0) + assert float(energies[0] - energies.min()) > 1.0 + if element in low_quality: + continue + r_min, r_max = eval_window(element, float(distances[-1])) + window = (distances >= r_min) & (distances <= r_max) + radii = distances[window] + window_energies = energies[window] + if float(window_energies[-1] - window_energies.min()) < 0.05: + continue + minimum = int(np.argmin(window_energies)) + assert 0 < minimum < len(window_energies) - 1 + assert radii[0] < radii[minimum] < radii[-1] + + def test_dummy_scales_are_positive(): dummy = reference_dummy_scales() assert dummy["bond_length_mae"] == pytest.approx(0.6068, abs=1e-3) From c6e983a265a8b20409d5c0978346925e43d94483 Mon Sep 17 00:00:00 2001 From: Anyang Peng <137014849+anyangml@users.noreply.github.com> Date: Wed, 30 Sep 2026 11:59:26 +0800 Subject: [PATCH 4/9] Keep only PBE diatomic curves that match the 50-point design grid. The published file deleted 45 distances across 11 curves, and the raw spin candidates are not available to fill those holes. Scoring a gappy curve would treat the deletion as if that geometry had never been computed. Co-authored-by: Cursor --- .../tasks/calculator/diatomics/diatomics.json | 1098 ----------------- .../tasks/calculator/diatomics/diatomics.py | 43 +- tests/tasks/calculator/test_diatomics.py | 34 +- 3 files changed, 61 insertions(+), 1114 deletions(-) diff --git a/lambench/tasks/calculator/diatomics/diatomics.json b/lambench/tasks/calculator/diatomics/diatomics.json index 3d39c3a..e241873 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.json +++ b/lambench/tasks/calculator/diatomics/diatomics.json @@ -2915,112 +2915,6 @@ -4.029337 ] }, - { - "element": "Ni", - "method": "PBE", - "R": [ - 0.992, - 1.029114, - 1.067617, - 1.10756, - 1.148997, - 1.191985, - 1.236581, - 1.282846, - 1.330841, - 1.380633, - 1.432287, - 1.485873, - 1.541465, - 1.599136, - 1.658965, - 1.721033, - 1.785422, - 1.852221, - 1.921519, - 1.993409, - 2.067989, - 2.145359, - 2.225624, - 2.308893, - 2.395276, - 2.484891, - 2.577859, - 2.674306, - 2.77436, - 2.878158, - 2.98584, - 3.09755, - 3.21344, - 3.333665, - 3.458389, - 3.587779, - 3.72201, - 3.861262, - 4.005725, - 4.155593, - 4.311067, - 4.472359, - 4.639685, - 4.813271, - 4.993351, - 5.180169, - 5.373977, - 5.783616, - 6.0 - ], - "E": [ - 80.891923, - 65.215043, - 52.345752, - 41.78056, - 33.094906, - 25.937228, - 20.01951, - 15.110167, - 11.029227, - 7.638933, - 4.836498, - 2.542406, - 0.690822, - -0.775548, - -1.911162, - -2.767105, - -3.390728, - -3.823656, - -4.101518, - -4.254851, - -4.309669, - -4.287412, - -4.205988, - -4.080405, - -3.922312, - -3.741738, - -3.54626, - -3.342574, - -3.135501, - -2.908172, - -2.801884, - -2.621955, - -2.450343, - -2.302451, - -2.168838, - -2.059748, - -1.978789, - -1.904079, - -1.83725, - -1.777437, - -1.725945, - -1.68132, - -1.350905, - -1.323587, - -1.303091, - -1.525727, - -1.508375, - -1.509696, - -1.46558 - ] - }, { "element": "Cu", "method": "PBE", @@ -4209,112 +4103,6 @@ -4.437869 ] }, - { - "element": "Zr", - "method": "PBE", - "R": [ - 1.4, - 1.442203, - 1.485679, - 1.530465, - 1.576601, - 1.624128, - 1.673087, - 1.723523, - 1.775479, - 1.829001, - 1.884137, - 1.940934, - 1.999444, - 2.059718, - 2.121808, - 2.18577, - 2.251661, - 2.319537, - 2.38946, - 2.461491, - 2.535693, - 2.612132, - 2.690875, - 2.771992, - 2.855554, - 2.941636, - 3.030312, - 3.121661, - 3.215764, - 3.312704, - 3.412566, - 3.515438, - 3.621412, - 3.73058, - 3.843039, - 3.958888, - 4.078229, - 4.201168, - 4.458276, - 4.592671, - 4.731118, - 4.873738, - 5.020658, - 5.172007, - 5.327918, - 5.488529, - 5.653982, - 5.824422, - 6.0 - ], - "E": [ - 27.144447, - 20.481949, - 14.725711, - 9.839422, - 5.759579, - 2.408992, - -0.294911, - -2.434813, - -4.090171, - -5.334998, - -6.23481, - -7.01774, - -7.617734, - -8.022222, - -8.272942, - -8.405477, - -8.447138, - -8.415995, - -8.322813, - -8.174792, - -7.977985, - -7.850784, - -7.832491, - -7.766811, - -7.66166, - -7.524399, - -7.361822, - -7.180101, - -6.984765, - -6.780633, - -6.57187, - -6.361886, - -6.153401, - -5.94874, - -5.749938, - -5.558973, - -5.377253, - -5.205389, - -5.185694, - -5.095461, - -5.009921, - -4.929523, - -4.854529, - -4.785096, - -4.750857, - -4.731665, - -4.716611, - -4.70503, - -4.52337 - ] - }, { "element": "Nb", "method": "PBE", @@ -6259,110 +6047,6 @@ -2.901204 ] }, - { - "element": "Pr", - "method": "PBE", - "R": [ - 1.624, - 1.667896, - 1.712979, - 1.75928, - 1.806832, - 1.85567, - 1.905828, - 1.957342, - 2.010248, - 2.064585, - 2.12039, - 2.177703, - 2.236565, - 2.297019, - 2.359106, - 2.488361, - 2.555621, - 2.624698, - 2.695643, - 2.768505, - 2.843337, - 2.920191, - 2.999122, - 3.080188, - 3.163444, - 3.24895, - 3.336768, - 3.42696, - 3.519589, - 3.614722, - 3.712427, - 3.812772, - 3.91583, - 4.021673, - 4.130378, - 4.24202, - 4.35668, - 4.47444, - 4.595382, - 4.719593, - 4.847162, - 4.978179, - 5.112737, - 5.392863, - 5.53863, - 5.688337, - 5.842091, - 6.0 - ], - "E": [ - 10.059111, - 6.762964, - 4.127451, - 2.04863, - 0.436663, - -0.869585, - -1.997109, - -2.968832, - -3.703243, - -4.247638, - -4.640622, - -4.911875, - -5.083926, - -5.199071, - -5.440535, - -5.582575, - -5.638514, - -5.715111, - -5.76057, - -5.775534, - -5.764744, - -5.734571, - -5.684268, - -5.614768, - -5.531871, - -5.408256, - -5.190735, - -5.290245, - -5.270751, - -5.22545, - -5.17535, - -5.113652, - -5.043573, - -4.966946, - -4.885494, - -4.800668, - -4.727421, - -4.70584, - -4.680527, - -4.62899, - -4.62667, - -4.38231, - -4.25508, - -4.299611, - -6.772696, - -4.459294, - -4.433896, - -4.410679 - ] - }, { "element": "Nd", "method": "PBE", @@ -6471,208 +6155,6 @@ -7.348448 ] }, - { - "element": "Pm", - "method": "PBE", - "R": [ - 1.592, - 1.635695, - 1.68059, - 1.726717, - 1.77411, - 1.822803, - 1.872833, - 1.924237, - 1.977051, - 2.031315, - 2.087068, - 2.144351, - 2.203207, - 2.263678, - 2.325809, - 2.389645, - 2.455233, - 2.522621, - 2.591859, - 2.662997, - 2.736088, - 2.888343, - 2.967619, - 3.049071, - 3.132758, - 3.307086, - 3.397855, - 3.491116, - 3.586936, - 3.685386, - 3.786538, - 3.997247, - 4.106959, - 4.219682, - 4.335499, - 4.454494, - 4.576756, - 4.702373, - 4.831438, - 4.964046, - 5.100293, - 5.384109, - 5.683718, - 5.839718, - 6.0 - ], - "E": [ - 6.46401, - 3.649456, - 0.836694, - -1.273183, - -3.001367, - -3.349063, - -5.575203, - -5.938712, - -7.286224, - -7.900915, - -8.388526, - -8.768924, - -9.232501, - -9.327242, - -9.915345, - -10.121085, - -10.322168, - -10.381866, - -10.516922, - -10.486214, - -10.530117, - -10.60425, - -10.548826, - -10.671258, - -10.660706, - -10.615886, - -10.592494, - -10.442152, - -10.518834, - -10.297435, - -10.39648, - -10.253067, - -10.382503, - -10.25044, - -10.088182, - -10.092147, - -10.403045, - -10.296423, - -10.222367, - -10.212853, - -10.184962, - -10.133362, - -10.27336, - -10.086514, - -9.978008 - ] - }, - { - "element": "Sm", - "method": "PBE", - "R": [ - 1.584, - 1.627643, - 1.672489, - 1.71857, - 1.765921, - 1.814576, - 1.864572, - 1.915946, - 1.968735, - 2.022978, - 2.078716, - 2.13599, - 2.194842, - 2.255315, - 2.317454, - 2.381306, - 2.446917, - 2.514335, - 2.583611, - 2.654796, - 2.727942, - 2.803104, - 2.880336, - 2.959697, - 3.041244, - 3.21114, - 3.299615, - 3.390527, - 3.483944, - 3.579936, - 3.678572, - 3.779925, - 3.884072, - 3.991087, - 4.101052, - 4.214046, - 4.330153, - 4.449459, - 4.698024, - 4.827466, - 4.960475, - 5.097148, - 5.237587, - 5.381895, - 5.53018, - 5.68255, - 5.839118, - 6.0 - ], - "E": [ - 4.591078, - 1.265873, - -1.475725, - -3.744861, - -5.617222, - -7.162885, - -8.437612, - -9.480795, - -10.333068, - -11.0223, - -11.574924, - -12.010805, - -12.350528, - -12.720528, - -12.954915, - -13.2926, - -13.446464, - -13.60812, - -13.728375, - -13.871469, - -13.933428, - -13.975155, - -13.951152, - -14.004713, - -14.040493, - -14.166911, - -14.721687, - -14.114151, - -14.148568, - -14.116059, - -13.994287, - -13.975053, - -14.040989, - -14.049167, - -14.168741, - -14.084367, - -14.129659, - -14.203661, - -14.366565, - -14.191073, - -14.069867, - -14.093398, - -14.073919, - -14.118314, - -14.094906, - -13.960311, - -13.997747, - -14.049611 - ] - }, { "element": "Eu", "method": "PBE", @@ -6889,480 +6371,6 @@ -16.3512 ] }, - { - "element": "Tb", - "method": "PBE", - "R": [ - 1.552, - 1.595426, - 1.640067, - 1.685957, - 1.733131, - 1.781624, - 1.831475, - 1.882721, - 1.9354, - 1.989554, - 2.045223, - 2.102449, - 2.161277, - 2.22175, - 2.283916, - 2.347821, - 2.413515, - 2.481046, - 2.550467, - 2.62183, - 2.695191, - 2.770603, - 2.927819, - 3.00974, - 3.180525, - 3.269518, - 3.361001, - 3.455043, - 3.551717, - 3.651096, - 3.753256, - 3.858274, - 3.96623, - 4.077207, - 4.308564, - 4.553049, - 4.811408, - 5.084426, - 5.226691, - 5.372936, - 5.523274, - 5.677818, - 6.0 - ], - "E": [ - 5.703526, - 2.109436, - -0.940685, - -3.416779, - -5.424327, - -7.04039, - -8.366995, - -9.42217, - -10.243355, - -10.866235, - -11.486297, - -11.828945, - -12.226512, - -12.398787, - -12.602744, - -12.698924, - -12.750438, - -12.772534, - -12.673655, - -12.82201, - -12.943329, - -13.14903, - -13.157576, - -13.28424, - -13.187637, - -13.204049, - -13.284752, - -13.345578, - -13.388598, - -13.499349, - -13.430648, - -13.426707, - -13.49084, - -13.471633, - -13.370262, - -13.313523, - -13.251349, - -13.652845, - -13.009275, - -13.004057, - -13.086068, - -13.011721, - -13.081735 - ] - }, - { - "element": "Dy", - "method": "PBE", - "R": [ - 1.536, - 1.579312, - 1.623845, - 1.669635, - 1.716715, - 1.765123, - 1.814896, - 1.866072, - 1.918691, - 1.972794, - 2.028423, - 2.085621, - 2.144431, - 2.204899, - 2.267073, - 2.331, - 2.396729, - 2.464312, - 2.533801, - 2.605249, - 2.678711, - 2.754246, - 2.83191, - 2.911764, - 2.993869, - 3.07829, - 3.165092, - 3.254341, - 3.346107, - 3.44046, - 3.537474, - 3.637224, - 3.739786, - 3.84524, - 4.065154, - 4.179783, - 4.297644, - 4.418829, - 4.543431, - 4.671546, - 4.803274, - 4.938717, - 5.077978, - 5.221167, - 5.368393, - 5.519771, - 5.835452, - 6.0 - ], - "E": [ - 10.141428, - 6.298558, - 3.151997, - 0.481388, - -1.688368, - -3.437448, - -4.833246, - -5.93257, - -6.786566, - -7.438144, - -7.925514, - -8.279458, - -8.649434, - -8.954991, - -9.184734, - -9.356472, - -9.489488, - -9.597563, - -9.689464, - -9.934005, - -10.014255, - -10.160445, - -10.147501, - -10.2471, - -10.344022, - -10.448461, - -10.52414, - -10.735713, - -12.247728, - -10.944088, - -11.030407, - -11.054485, - -11.116177, - -11.138981, - -11.143757, - -12.438084, - -11.149575, - -11.138335, - -11.124074, - -11.107639, - -11.089832, - -11.071528, - -11.052622, - -11.040809, - -13.034866, - -11.005295, - -10.976671, - -10.972019 - ] - }, - { - "element": "Ho", - "method": "PBE", - "R": [ - 1.536, - 1.579312, - 1.623845, - 1.669635, - 1.716715, - 1.765123, - 1.814896, - 1.866072, - 1.918691, - 1.972794, - 2.028423, - 2.085621, - 2.144431, - 2.204899, - 2.267073, - 2.331, - 2.396729, - 2.464312, - 2.533801, - 2.605249, - 2.678711, - 2.754246, - 2.911764, - 2.993869, - 3.07829, - 3.165092, - 3.254341, - 3.346107, - 3.537474, - 3.637224, - 3.739786, - 3.84524, - 3.953668, - 4.065154, - 4.179783, - 4.297644, - 4.418829, - 4.543431, - 4.671546, - 4.803274, - 4.938717, - 5.077978, - 5.221167, - 5.368393, - 5.519771, - 5.675417, - 6.0 - ], - "E": [ - 7.354175, - 5.419177, - 4.017431, - 1.178728, - 1.060191, - -0.592992, - -1.554301, - -2.52773, - -3.446821, - -4.186757, - -4.841196, - -5.369594, - -5.783033, - -6.105004, - -6.4007, - -6.662356, - -6.880399, - -7.069934, - -7.23384, - -7.176727, - -73.737041, - -80.317247, - -13.134028, - -13.606465, - -7.982146, - -8.014743, - -8.878956, - -9.769848, - -8.793312, - -8.728927, - -8.777978, - -8.813677, - -12.483617, - -14.413129, - -10.289441, - -8.863603, - -8.859846, - -8.851657, - -8.841181, - -8.82795, - -8.813855, - -8.798934, - -8.783957, - -9.474133, - -11.585835, - -8.937198, - -10.014047 - ] - }, - { - "element": "Er", - "method": "PBE", - "R": [ - 1.512, - 1.555135, - 1.645132, - 1.692065, - 1.740337, - 1.841052, - 1.893574, - 1.947595, - 2.003157, - 2.060304, - 2.179535, - 2.241714, - 2.305667, - 2.371444, - 2.439097, - 2.58025, - 2.653861, - 2.807442, - 2.887534, - 2.969911, - 3.141782, - 3.231412, - 3.418416, - 3.616243, - 3.825518, - 3.934654, - 4.046903, - 4.162355, - 4.403234, - 4.528852, - 4.658053, - 4.927618, - 5.068195, - 5.212783, - 5.361496, - 5.514451, - 5.67177, - 6.0 - ], - "E": [ - 0.0, - 3.036848, - 4.727016, - 3.312232, - 1.002699, - 0.428345, - -0.512458, - -1.283042, - -1.908911, - -5.58074, - -3.377302, - -3.570067, - -3.815565, - -4.108729, - -5.502681, - -6.67225, - -5.002933, - -5.338907, - -7.146323, - -9.004307, - -19.916635, - -24.933959, - -8.18526, - -8.989307, - -8.80971, - -12.422998, - -11.241471, - -6.379608, - -44.201271, - -38.933082, - -17.627718, - -6.184489, - -6.172653, - -6.158868, - -6.145159, - -6.131576, - -0.662667, - -4.665525 - ] - }, - { - "element": "Tm", - "method": "PBE", - "R": [ - 1.52, - 1.607617, - 1.653302, - 1.700285, - 1.748603, - 1.798295, - 1.849398, - 1.901954, - 1.956003, - 2.011588, - 2.068753, - 2.127542, - 2.188002, - 2.314125, - 2.379887, - 2.447518, - 2.517071, - 2.662162, - 2.737814, - 2.815617, - 2.89563, - 3.062543, - 3.149573, - 3.239077, - 3.331124, - 3.52314, - 3.62326, - 3.726224, - 3.832115, - 3.941015, - 4.05301, - 4.286637, - 4.408454, - 4.533732, - 4.795069, - 5.071471, - 5.215591, - 5.516233, - 5.672992, - 5.834205, - 6.0 - ], - "E": [ - 0.0, - 8.596629, - 7.360087, - 5.832307, - 4.444998, - 3.671645, - 2.240228, - 1.186179, - 0.765312, - -0.330165, - -0.865769, - -0.961273, - -2.36011, - -2.054048, - -7.485039, - -5.765144, - -3.637783, - -2.860933, - -3.01762, - -3.161861, - -5.704531, - -4.438255, - -5.480567, - -13.95281, - -4.893757, - -4.134657, - -5.46284, - -6.439533, - -4.587596, - -4.674003, - -4.664614, - -5.901758, - -21.515841, - -12.652671, - -4.646476, - -7.11694, - -7.037609, - -3.780211, - -12.101119, - -5.234524, - -4.151942 - ] - }, { "element": "Yb", "method": "PBE", @@ -8119,112 +7127,6 @@ -5.761469 ] }, - { - "element": "Ir", - "method": "PBE", - "R": [ - 1.128, - 1.167138, - 1.207634, - 1.249535, - 1.29289, - 1.337749, - 1.384165, - 1.432191, - 1.481883, - 1.5333, - 1.5865, - 1.641547, - 1.698503, - 1.757436, - 1.818413, - 1.881506, - 1.946789, - 2.014336, - 2.084227, - 2.156543, - 2.231368, - 2.308789, - 2.388897, - 2.471784, - 2.557547, - 2.646286, - 2.738103, - 2.833107, - 2.931406, - 3.033117, - 3.138356, - 3.247247, - 3.359916, - 3.476495, - 3.597118, - 3.851065, - 3.984685, - 4.122941, - 4.265994, - 4.41401, - 4.567162, - 4.725628, - 4.889592, - 5.059245, - 5.234785, - 5.416415, - 5.604347, - 5.7988, - 6.0 - ], - "E": [ - 69.446736, - 58.364789, - 48.19382, - 39.322295, - 31.672753, - 25.111264, - 19.498984, - 14.7028, - 10.59427, - 7.061239, - 4.018104, - 1.409288, - -0.795609, - -2.612572, - -4.056097, - -5.355573, - -6.41088, - -7.143877, - -7.59721, - -7.81621, - -7.845391, - -7.725755, - -7.493192, - -7.178074, - -6.806519, - -6.402254, - -5.983263, - -5.605853, - -5.2564, - -4.919687, - -4.600284, - -4.304048, - -4.032199, - -3.787068, - -3.568379, - -3.657383, - -3.544425, - -3.444124, - -3.355822, - -3.278515, - -3.211114, - -3.154095, - -3.115822, - -3.090979, - -3.073805, - -3.06147, - -3.052402, - -3.045585, - -3.040387 - ] - }, { "element": "Pt", "method": "PBE", diff --git a/lambench/tasks/calculator/diatomics/diatomics.py b/lambench/tasks/calculator/diatomics/diatomics.py index 99b4aaf..e03d514 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.py +++ b/lambench/tasks/calculator/diatomics/diatomics.py @@ -21,8 +21,12 @@ Project MP24 static settings in a 15 Å cell. Distances run geometrically from 0.8 covalent radii to 6 Å. At each distance the stored energy is the lowest among an even-NUPDOWN spin ladder plus one antiferromagnetic -candidate. The file checked in here keeps the PBE distances and energies -for H–U except Po, At, Rn, Fr, and Ra (87 elements). +candidate. Po, At, Rn, Fr, and Ra are outside the scored set. The public +file does not include the raw spin-candidate curves or a per-point edit +log, so a deleted distance cannot be restored and a replaced energy cannot +be detected. This task therefore keeps a PBE curve only when it is exactly +the 50-point design grid. That drops Ni, Zr, Pr, Pm, Sm, Tb, Dy, Ho, Er, +Tm, and Ir (45 missing distances). 76 curves remain. Three per-element metrics are averaged over finite values: @@ -69,6 +73,9 @@ _LABEL_FILE = Path(__file__).parent / "diatomics.json" _EXCLUDED_ELEMENTS = frozenset({"Po", "At", "Rn", "Fr", "Ra"}) +_N_GRID = 50 +_GRID_R_MAX = 6.0 +_GRID_ATOL = 1e-6 _WALL_THRESHOLDS_EV = (1.0, 5.0, 10.0, 20.0, 50.0, 100.0) _MIN_BINDING_EV = 0.05 _MIN_ENERGY_JUMP_EV = 1.5 @@ -80,6 +87,22 @@ _BOX_ANGSTROM = 30.0 +def design_distances(element: str) -> np.ndarray: + """50 geometric separations from 0.8 covalent radii to 6 Å.""" + r_min = 0.8 * float(covalent_radii[atomic_numbers[element]]) + return r_min * (_GRID_R_MAX / r_min) ** (np.arange(_N_GRID) / (_N_GRID - 1)) + + +def matches_design_grid(element: str, distances: np.ndarray) -> bool: + """True when ``distances`` is the design grid and nothing else.""" + distances = np.asarray(distances, dtype=float) + if distances.size != _N_GRID: + return False + return bool( + np.allclose(distances, design_distances(element), rtol=0, atol=_GRID_ATOL) + ) + + def eval_window( element: str, seps_max: float, *, r_min_factor: float = 0.9 ) -> tuple[float, float]: @@ -269,7 +292,12 @@ def score_curve( def load_reference( path: str | None = None, ) -> dict[str, tuple[np.ndarray, np.ndarray]]: - """Scored PBE curves keyed by element, distances then energies in Å and eV.""" + """Scored PBE curves keyed by element, distances then energies in Å and eV. + + Curves that are not the 50-point design grid are omitted. The published + reference deleted those distances, and the missing energies are not in + the file. + """ label_path = _LABEL_FILE if path is None else Path(path) with open(label_path) as fh: raw: list[dict] = json.load(fh) @@ -278,10 +306,11 @@ def load_reference( element = entry["element"] if element in _EXCLUDED_ELEMENTS: continue - curves[element] = ( - np.asarray(entry["R"], dtype=float), - np.asarray(entry["E"], dtype=float), - ) + distances = np.asarray(entry["R"], dtype=float) + if not matches_design_grid(element, distances): + logging.warning(f"{element} is not the 50-point design grid; skipping") + continue + curves[element] = (distances, np.asarray(entry["E"], dtype=float)) return curves diff --git a/tests/tasks/calculator/test_diatomics.py b/tests/tasks/calculator/test_diatomics.py index 6828562..e3551bf 100644 --- a/tests/tasks/calculator/test_diatomics.py +++ b/tests/tasks/calculator/test_diatomics.py @@ -4,6 +4,7 @@ from lambench.metrics.utils import aggregated_diatomics_results from lambench.tasks.calculator.diatomics.diatomics import ( + design_distances, eval_window, load_reference, low_quality_elements, @@ -41,14 +42,29 @@ def _full_results(**overrides): def test_scored_set_and_reference_gate(): names = scored_element_names() - assert len(names) == 87 + assert len(names) == 76 assert names[0] == "H" assert names[-1] == "U" - for element in ("Po", "At", "Rn", "Fr", "Ra"): + for element in ( + "Po", + "At", + "Rn", + "Fr", + "Ra", + "Ni", + "Zr", + "Pr", + "Pm", + "Sm", + "Tb", + "Dy", + "Ho", + "Er", + "Tm", + "Ir", + ): assert element not in names - assert low_quality_elements() == frozenset( - {"Pr", "Pm", "Sm", "Tb", "Dy", "Ho", "Er", "Tm"} - ) + assert low_quality_elements() == frozenset() distances, energies = load_reference()["H"] assert distances[0] == pytest.approx( 0.8 * float(covalent_radii[atomic_numbers["H"]]) @@ -67,8 +83,8 @@ def test_reference_curves_have_a_wall_and_an_interior_minimum(): low_quality = low_quality_elements() for element, (distances, energies) in load_reference().items(): rcov = float(covalent_radii[atomic_numbers[element]]) - assert np.all(np.diff(distances) > 0) - assert np.isfinite(distances).all() + assert len(distances) == len(energies) == 50 + assert np.allclose(distances, design_distances(element), rtol=0, atol=1e-6) assert np.isfinite(energies).all() assert distances[0] == pytest.approx(0.8 * rcov) assert distances[-1] == pytest.approx(6.0) @@ -88,8 +104,8 @@ def test_reference_curves_have_a_wall_and_an_interior_minimum(): def test_dummy_scales_are_positive(): dummy = reference_dummy_scales() - assert dummy["bond_length_mae"] == pytest.approx(0.6068, abs=1e-3) - assert dummy["wall_dist_mae"] == pytest.approx(0.2733, abs=1e-3) + assert dummy["bond_length_mae"] == pytest.approx(0.6186, abs=1e-3) + assert dummy["wall_dist_mae"] == pytest.approx(0.2802, abs=1e-3) def test_identical_parabola_has_zero_geometry_error(): From 4e8aab59a78f5238943506914d8e3bfddb2897ba Mon Sep 17 00:00:00 2001 From: Anyang Peng <137014849+anyangml@users.noreply.github.com> Date: Wed, 30 Sep 2026 13:40:13 +0800 Subject: [PATCH 5/9] Load the diatomic reference as stored in the JSON file. The 76 complete PBE curves are already the contents of diatomics.json, so the loader no longer drops elements or rebuilds the design grid on each read. Co-authored-by: Cursor --- .../tasks/calculator/diatomics/diatomics.py | 49 ++++++------------- 1 file changed, 14 insertions(+), 35 deletions(-) diff --git a/lambench/tasks/calculator/diatomics/diatomics.py b/lambench/tasks/calculator/diatomics/diatomics.py index e03d514..5cb786b 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.py +++ b/lambench/tasks/calculator/diatomics/diatomics.py @@ -21,12 +21,12 @@ Project MP24 static settings in a 15 Å cell. Distances run geometrically from 0.8 covalent radii to 6 Å. At each distance the stored energy is the lowest among an even-NUPDOWN spin ladder plus one antiferromagnetic -candidate. Po, At, Rn, Fr, and Ra are outside the scored set. The public -file does not include the raw spin-candidate curves or a per-point edit -log, so a deleted distance cannot be restored and a replaced energy cannot -be detected. This task therefore keeps a PBE curve only when it is exactly -the 50-point design grid. That drops Ni, Zr, Pr, Pm, Sm, Tb, Dy, Ho, Er, -Tm, and Ir (45 missing distances). 76 curves remain. +candidate. The public file has no raw spin-candidate curves and no +per-point edit log, so a deleted distance cannot be restored. Po, At, Rn, +Fr, and Ra are not part of the scored set, and Ni, Zr, Pr, Pm, Sm, Tb, Dy, +Ho, Er, Tm, and Ir are missing 45 design-grid distances. Those curves are +already absent from ``diatomics.json``. The loader reads that file as +stored. Three per-element metrics are averaged over finite values: @@ -72,10 +72,8 @@ from lambench.models.ase_models import ASEModel _LABEL_FILE = Path(__file__).parent / "diatomics.json" -_EXCLUDED_ELEMENTS = frozenset({"Po", "At", "Rn", "Fr", "Ra"}) _N_GRID = 50 _GRID_R_MAX = 6.0 -_GRID_ATOL = 1e-6 _WALL_THRESHOLDS_EV = (1.0, 5.0, 10.0, 20.0, 50.0, 100.0) _MIN_BINDING_EV = 0.05 _MIN_ENERGY_JUMP_EV = 1.5 @@ -93,16 +91,6 @@ def design_distances(element: str) -> np.ndarray: return r_min * (_GRID_R_MAX / r_min) ** (np.arange(_N_GRID) / (_N_GRID - 1)) -def matches_design_grid(element: str, distances: np.ndarray) -> bool: - """True when ``distances`` is the design grid and nothing else.""" - distances = np.asarray(distances, dtype=float) - if distances.size != _N_GRID: - return False - return bool( - np.allclose(distances, design_distances(element), rtol=0, atol=_GRID_ATOL) - ) - - def eval_window( element: str, seps_max: float, *, r_min_factor: float = 0.9 ) -> tuple[float, float]: @@ -292,26 +280,17 @@ def score_curve( def load_reference( path: str | None = None, ) -> dict[str, tuple[np.ndarray, np.ndarray]]: - """Scored PBE curves keyed by element, distances then energies in Å and eV. - - Curves that are not the 50-point design grid are omitted. The published - reference deleted those distances, and the missing energies are not in - the file. - """ + """Scored PBE curves keyed by element, distances then energies in Å and eV.""" label_path = _LABEL_FILE if path is None else Path(path) with open(label_path) as fh: raw: list[dict] = json.load(fh) - curves: dict[str, tuple[np.ndarray, np.ndarray]] = {} - for entry in raw: - element = entry["element"] - if element in _EXCLUDED_ELEMENTS: - continue - distances = np.asarray(entry["R"], dtype=float) - if not matches_design_grid(element, distances): - logging.warning(f"{element} is not the 50-point design grid; skipping") - continue - curves[element] = (distances, np.asarray(entry["E"], dtype=float)) - return curves + return { + entry["element"]: ( + np.asarray(entry["R"], dtype=float), + np.asarray(entry["E"], dtype=float), + ) + for entry in raw + } def scored_element_names(path: str | None = None) -> tuple[str, ...]: From 5a8a874bb302fabcb29f01bb1ae92e5321493bc6 Mon Sep 17 00:00:00 2001 From: Anyang Peng <137014849+anyangml@users.noreply.github.com> Date: Wed, 30 Sep 2026 15:38:43 +0800 Subject: [PATCH 6/9] Simplify diatomic scoring around bond geometry and well depth. Replace wall-radius and boolean flip metrics with interpretable well-depth error and raw force-flip counts, using explicit blind baselines for aggregation. Co-authored-by: Cursor --- lambench/metrics/utils.py | 63 +++--- .../tasks/calculator/diatomics/diatomics.py | 208 ++++-------------- tests/tasks/calculator/test_diatomics.py | 123 +++++------ 3 files changed, 141 insertions(+), 253 deletions(-) diff --git a/lambench/metrics/utils.py b/lambench/metrics/utils.py index 9e4c862..c2dd568 100644 --- a/lambench/metrics/utils.py +++ b/lambench/metrics/utils.py @@ -128,7 +128,7 @@ def aggregated_nve_md_results(results: dict[str, dict[str, float]]) -> dict[str, ## Inference efficiency utility functions def aggregated_inference_efficiency_results( results: dict[str, dict[str, float]], -) -> dict[str, float]: +) -> dict[str, float | None]: system_level_avg = [] system_level_std = [] system_level_success_rate = [] @@ -143,19 +143,20 @@ def aggregated_inference_efficiency_results( if success_count != len(results): return {"average_time": None, "std_time": None, "success_rate": 0.0} return { - "average_time": np.round(np.mean(system_level_avg), 6), - "standard_deviation": np.round( - np.sqrt(np.mean(np.square(system_level_std))), 6 + "average_time": float(np.round(np.mean(system_level_avg), 6)), + "standard_deviation": float( + np.round(np.sqrt(np.mean(np.square(system_level_std))), 6) ), - "success_rate": np.round(np.mean(system_level_success_rate), 2), + "success_rate": float(np.round(np.mean(system_level_success_rate), 2)), } +## Diatomic utility functions def _empty_diatomics_agg() -> dict[str, float | None]: return { "bond_length_mae": None, - "wall_dist_mae": None, - "force_flip_rate": None, + "well_depth_mae": None, + "force_flip_count": None, "coverage": 0.0, "score": None, } @@ -176,20 +177,20 @@ def _dummy_hat(value: float | None, dummy: float) -> float | None: def _element_is_excluded(record: dict | None) -> bool: if not isinstance(record, dict) or record.get("model_excluded", True): return True - flip = record.get("force_flip_fail") + flip = record.get("force_flip_count") return flip is None or not np.isfinite(flip) def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float | None]: """Aggregate per-element diatomic curves into one coverage-weighted score. - bond_length_mae and wall_dist_mae are raw MAEs in Å. force_flip_rate is - the fraction of scored elements whose Fx changes sign other than once. - Each geometry MAE is divided by the PBE mean-predictor dummy and capped - at 1. score is the equal-weight average of those three terms divided by - coverage. Missing elements and model_excluded curves lower coverage - instead of discarding the model. score is None when coverage is zero or - any of the three terms has no finite samples. + bond_length_mae is in Å and well_depth_mae is in eV. force_flip_count is + the mean number of sign changes in first-atom Fx. Bond and well errors are + divided by their blind dummy scales; force flips use the mean per-element + absolute deviation from the ideal count of one. Each term is capped at 1. + score is their equal-weight average divided by coverage. Missing elements + and model_excluded curves lower coverage instead of discarding the model. + score is None when coverage is zero or any term has no finite samples. """ from lambench.tasks.calculator.diatomics.diatomics import ( reference_dummy_scales, @@ -201,37 +202,43 @@ def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float | return _empty_diatomics_agg() bond_errors: list[float] = [] - wall_errors: list[float] = [] - flip_fails: list[float] = [] + well_errors: list[float] = [] + flip_counts: list[float] = [] + flip_deviations: list[float] = [] n_excluded = 0 for name in names: record = None if not results else results.get(name) if _element_is_excluded(record): n_excluded += 1 continue + assert isinstance(record, dict) bond = record.get("bond_length_error") - wall = record.get("wall_dist_error") + well = record.get("well_depth_error") if bond is not None and np.isfinite(bond): bond_errors.append(float(bond)) - if wall is not None and np.isfinite(wall): - wall_errors.append(float(wall)) - flip_fails.append(float(record["force_flip_fail"])) + if well is not None and np.isfinite(well): + well_errors.append(float(well)) + flip_count = float(record["force_flip_count"]) + flip_counts.append(flip_count) + flip_deviations.append(abs(flip_count - 1.0)) coverage = (len(names) - n_excluded) / len(names) bond_mae = _mean_or_none(bond_errors) - wall_mae = _mean_or_none(wall_errors) - flip_rate = _mean_or_none(flip_fails) + well_mae = _mean_or_none(well_errors) + flip_count = _mean_or_none(flip_counts) + flip_deviation = _mean_or_none(flip_deviations) dummy = reference_dummy_scales() bond_hat = _dummy_hat(bond_mae, dummy["bond_length_mae"]) - wall_hat = _dummy_hat(wall_mae, dummy["wall_dist_mae"]) - if coverage <= 0 or bond_hat is None or wall_hat is None or flip_rate is None: + well_hat = _dummy_hat(well_mae, dummy["well_depth_mae"]) + flip_hat = _dummy_hat(flip_deviation, dummy["force_flip_deviation"]) + if coverage <= 0 or bond_hat is None or well_hat is None or flip_hat is None: score = None else: - score = float((bond_hat + wall_hat + flip_rate) / (3.0 * coverage)) + score = float((bond_hat + well_hat + flip_hat) / (3.0 * coverage)) return { "bond_length_mae": bond_mae, - "wall_dist_mae": wall_mae, - "force_flip_rate": flip_rate, + "well_depth_mae": well_mae, + "force_flip_count": flip_count, "coverage": float(coverage), "score": score, } diff --git a/lambench/tasks/calculator/diatomics/diatomics.py b/lambench/tasks/calculator/diatomics/diatomics.py index 5cb786b..3368de3 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.py +++ b/lambench/tasks/calculator/diatomics/diatomics.py @@ -32,25 +32,22 @@ - bond_length_error: absolute equilibrium-distance error in Å. The distance is a quadratic fit of up to five points around the minimum. Reference - binding energies below 0.05 eV are skipped, as are PBE curves that fail - the smoothness gate. -- wall_dist_error: MAE in Å of repulsive-branch radii at 1, 5, 10, 20, 50, - and 100 eV above the well. A threshold the reference reaches and the - model does not contributes the full reference radius. -- force_flip_fail: 0 when Fx on the first atom changes sign exactly once - after dropping |Fx| < 0.01 eV/Å, otherwise 1. - -Geometry metrics use distances from 0.9 covalent radii to -min(3.1 Alvarez vdW radii, 6 Å). The wall metric extends the lower bound -to 0.8 covalent radii. A model curve that is non-finite in that range, or -that has an energy jump of at least 1.5 eV and at least three -energy-difference sign flips, is dropped from the averages and lowers -coverage. + binding energies below 0.05 eV are skipped. +- well_depth_error: absolute error in eV of + ``E(r_far) - min(E)`` over the evaluation window. It is skipped with + bond length when the reference binding energy is below 0.05 eV. +- force_flip_count: number of Fx sign changes on the first atom after + dropping |Fx| < 0.01 eV/Å. + +All metrics use distances from 0.9 covalent radii to +min(3.1 Alvarez vdW radii, 6 Å). A model curve that is non-finite in that +range is dropped from the averages and lowers coverage. The leaderboard score is the equal-weight average of the dummy-normalized -bond-length MAE, the dummy-normalized wall MAE, and the force-flip rate, -divided by coverage. The dummy predicts each geometric quantity by the -mean of the PBE reference. Zero is a perfect single-well match. +bond-length MAE, dummy-normalized well-depth MAE, and mean absolute +deviation from one force flip, divided by coverage. The bond-length dummy +guesses the midpoint of each element's evaluation window. The well-depth +dummy predicts zero binding energy. Zero is a perfect single-well match. Reference file: lambench/tasks/calculator/diatomics/diatomics.json """ @@ -74,13 +71,9 @@ _LABEL_FILE = Path(__file__).parent / "diatomics.json" _N_GRID = 50 _GRID_R_MAX = 6.0 -_WALL_THRESHOLDS_EV = (1.0, 5.0, 10.0, 20.0, 50.0, 100.0) _MIN_BINDING_EV = 0.05 -_MIN_ENERGY_JUMP_EV = 1.5 -_MIN_ENERGY_FLIPS = 3 _MIN_WINDOW_POINTS = 5 _FORCE_SIGN_THRESHOLD = 1e-2 -_ENERGY_DIFF_THRESHOLD = 1e-3 _FIT_POINTS = 5 _BOX_ANGSTROM = 30.0 @@ -108,30 +101,6 @@ def eval_window( return r_min, r_max -def _energy_smoothness(energies: np.ndarray) -> tuple[float, int]: - """Energy jump (eV) at sign flips, and the flip count. - - Differences smaller than 1e-3 eV are treated as zero, matching the - Matbench Discovery gate. - """ - diffs = np.diff(np.asarray(energies, dtype=float)) - diffs[np.abs(diffs) < _ENERGY_DIFF_THRESHOLD] = 0 - signs = np.sign(diffs) - kept = signs != 0 - diffs = diffs[kept] - signs = signs[kept] - if signs.size < 2: - return 0.0, 0 - flips = np.diff(signs) != 0 - jump = float(np.abs(diffs[:-1][flips]).sum() + np.abs(diffs[1:][flips]).sum()) - return jump, int(np.sum(flips)) - - -def _fails_smoothness_gate(energies: np.ndarray) -> bool: - jump, n_flips = _energy_smoothness(energies) - return jump >= _MIN_ENERGY_JUMP_EV and n_flips >= _MIN_ENERGY_FLIPS - - def _binding_energy(energies: np.ndarray) -> float: return float(energies[-1] - np.min(energies)) @@ -155,42 +124,6 @@ def _equilibrium_distance(seps: np.ndarray, energies: np.ndarray) -> float: return float(seps[min_idx]) -def _repulsive_radius( - seps: np.ndarray, energies: np.ndarray, threshold_ev: float -) -> float: - """Invert the repulsive branch to the radius at E_min + threshold_ev.""" - min_idx = int(np.argmin(energies)) - if min_idx == 0: - return np.nan - radii_inward = seps[min_idx::-1] - energy_above_min = energies[min_idx::-1] - energies[min_idx] - monotonic_energy = np.maximum.accumulate(energy_above_min) - unique_energy, unique_idx = np.unique(monotonic_energy, return_index=True) - if len(unique_energy) < 2 or threshold_ev > unique_energy[-1]: - return np.nan - return float(np.interp(threshold_ev, unique_energy, radii_inward[unique_idx])) - - -def _wall_distance_mae( - ref_seps: np.ndarray, - ref_energies: np.ndarray, - model_seps: np.ndarray, - model_energies: np.ndarray, -) -> float | None: - errors: list[float] = [] - for threshold in _WALL_THRESHOLDS_EV: - radius_ref = _repulsive_radius(ref_seps, ref_energies, threshold) - if not np.isfinite(radius_ref): - continue - radius_pred = _repulsive_radius(model_seps, model_energies, threshold) - errors.append( - abs(radius_pred - radius_ref) if np.isfinite(radius_pred) else radius_ref - ) - if not errors: - return None - return float(np.mean(errors)) - - def _force_flip_count(force_x: np.ndarray) -> int: kept = force_x[np.abs(force_x) >= _FORCE_SIGN_THRESHOLD] if kept.size < 2: @@ -201,8 +134,8 @@ def _force_flip_count(force_x: np.ndarray) -> int: def _excluded_record() -> dict[str, float | int | bool | None]: return { "bond_length_error": None, - "wall_dist_error": None, - "force_flip_fail": None, + "well_depth_error": None, + "force_flip_count": None, "model_excluded": True, } @@ -213,14 +146,10 @@ def score_curve( ref_energies: np.ndarray, model_energies: np.ndarray, model_force_x: np.ndarray, - *, - reference_low_quality: bool, ) -> dict[str, float | int | bool | None]: """Score one homonuclear curve against its PBE reference. ``model_excluded`` is true when the model curve cannot be scored. - Reference-quality skips leave ``model_excluded`` false and set the - PBE-relative errors to None. """ distances = np.asarray(distances, dtype=float) ref_energies = np.asarray(ref_energies, dtype=float) @@ -235,44 +164,33 @@ def score_curve( seps_max = float(distances[-1]) r_min, r_max = eval_window(element, seps_max) - wall_r_min = eval_window(element, seps_max, r_min_factor=0.8)[0] - 1e-12 general = (distances >= r_min) & (distances <= r_max) - wall = (distances >= wall_r_min) & (distances <= r_max) - if int(general.sum()) < _MIN_WINDOW_POINTS or int(wall.sum()) < 2: + if int(general.sum()) < _MIN_WINDOW_POINTS: return _excluded_record() if not ( - np.isfinite(model_energies[wall]).all() - and np.isfinite(model_force_x[wall]).all() + np.isfinite(model_energies[general]).all() + and np.isfinite(model_force_x[general]).all() ): return _excluded_record() - if not np.isfinite(ref_energies[wall]).all(): - return _excluded_record() - if _fails_smoothness_gate(model_energies[general]): + if not np.isfinite(ref_energies[general]).all(): return _excluded_record() n_flips = _force_flip_count(model_force_x[general]) result: dict[str, float | int | bool | None] = { "bond_length_error": None, - "wall_dist_error": None, - "force_flip_fail": 0 if n_flips == 1 else 1, + "well_depth_error": None, + "force_flip_count": n_flips, "model_excluded": False, } - if reference_low_quality: - return result - - result["wall_dist_error"] = _wall_distance_mae( - distances[wall], - ref_energies[wall], - distances[wall], - model_energies[wall], - ) + ref_general = ref_energies[general] - if _binding_energy(ref_general) >= _MIN_BINDING_EV: + model_general = model_energies[general] + ref_depth = _binding_energy(ref_general) + if ref_depth >= _MIN_BINDING_EV: ref_distance = _equilibrium_distance(distances[general], ref_general) - model_distance = _equilibrium_distance( - distances[general], model_energies[general] - ) + model_distance = _equilibrium_distance(distances[general], model_general) result["bond_length_error"] = float(abs(model_distance - ref_distance)) + result["well_depth_error"] = abs(_binding_energy(model_general) - ref_depth) return result @@ -297,68 +215,34 @@ def scored_element_names(path: str | None = None) -> tuple[str, ...]: return tuple(load_reference(path)) -@lru_cache -def low_quality_elements(path: str | None = None) -> frozenset[str]: - """PBE curves too jumpy to use as a geometry reference.""" - flagged: set[str] = set() - for element, (distances, energies) in load_reference(path).items(): - r_min, r_max = eval_window(element, float(np.max(distances))) - mask = (distances >= r_min) & (distances <= r_max) - if int(mask.sum()) < _MIN_WINDOW_POINTS: - continue - window = energies[mask] - if not np.isfinite(window).all() or _fails_smoothness_gate(window): - flagged.add(element) - return frozenset(flagged) - - @lru_cache def reference_dummy_scales(path: str | None = None) -> dict[str, float]: - """MAE of predicting each reference geometry by its cross-element mean. + """Metric scales from blind bond-position and flat-energy predictions. - Bond lengths use elements that pass the smoothness gate and bind by at - least 0.05 eV. Wall radii use every gated element that reaches a - threshold; the dummy error at each threshold is the deviation from the - mean radius at that threshold. + For each bound reference, the bond dummy guesses the arithmetic midpoint + of that element's evaluation window and the well-depth dummy predicts zero. + A flat force curve has zero flips, one away from the ideal count of one. """ - low_quality = low_quality_elements(path) - bond_lengths: list[float] = [] - radii: dict[float, dict[str, float]] = {t: {} for t in _WALL_THRESHOLDS_EV} + bond_errors: list[float] = [] + well_errors: list[float] = [] for element, (distances, energies) in load_reference(path).items(): - if element in low_quality: - continue seps_max = float(np.max(distances)) r_min, r_max = eval_window(element, seps_max) general = (distances >= r_min) & (distances <= r_max) - if ( - int(general.sum()) >= 3 - and _binding_energy(energies[general]) >= _MIN_BINDING_EV - ): - bond_lengths.append( - _equilibrium_distance(distances[general], energies[general]) - ) - wall_r_min = eval_window(element, seps_max, r_min_factor=0.8)[0] - 1e-12 - wall = (distances >= wall_r_min) & (distances <= r_max) - if int(wall.sum()) < 2: + if int(general.sum()) < 3: continue - for threshold in _WALL_THRESHOLDS_EV: - radius = _repulsive_radius(distances[wall], energies[wall], threshold) - if np.isfinite(radius): - radii[threshold][element] = radius - - bond = np.asarray(bond_lengths, dtype=float) - bond_dummy = float(np.mean(np.abs(bond - np.mean(bond)))) - per_element: dict[str, list[float]] = {} - for threshold, values in radii.items(): - if not values: + ref_energies = energies[general] + ref_depth = _binding_energy(ref_energies) + if ref_depth < _MIN_BINDING_EV: continue - mean_radius = float(np.mean(list(values.values()))) - for element, radius in values.items(): - per_element.setdefault(element, []).append(abs(radius - mean_radius)) - wall_errors = [float(np.mean(errs)) for errs in per_element.values() if errs] + ref_distance = _equilibrium_distance(distances[general], ref_energies) + bond_errors.append(abs((r_min + r_max) / 2 - ref_distance)) + well_errors.append(ref_depth) + return { - "bond_length_mae": bond_dummy, - "wall_dist_mae": float(np.mean(wall_errors)), + "bond_length_mae": float(np.mean(bond_errors)), + "well_depth_mae": float(np.mean(well_errors)), + "force_flip_deviation": 1.0, } @@ -395,7 +279,6 @@ def run_inference(model: ASEModel, test_data: Path | None = None) -> dict[str, d """Score model curves on the PBE homonuclear reference grid.""" label_path = None if test_data is None else str(test_data / "diatomics.json") reference = load_reference(label_path) - low_quality = low_quality_elements(label_path) calc = model.calc results: dict[str, dict] = {} for element, (distances, ref_energies) in reference.items(): @@ -406,7 +289,6 @@ def run_inference(model: ASEModel, test_data: Path | None = None) -> dict[str, d ref_energies, model_energies, model_force_x, - reference_low_quality=element in low_quality, ) results[element] = element_result logging.info(f"{element}: {element_result}") diff --git a/tests/tasks/calculator/test_diatomics.py b/tests/tasks/calculator/test_diatomics.py index e3551bf..025c7ad 100644 --- a/tests/tasks/calculator/test_diatomics.py +++ b/tests/tasks/calculator/test_diatomics.py @@ -7,7 +7,6 @@ design_distances, eval_window, load_reference, - low_quality_elements, reference_dummy_scales, score_curve, scored_element_names, @@ -26,8 +25,8 @@ def _parabola(element: str = "Si", shift: float = 0.0, scale: float = 80.0): def _record(**overrides): record = { "bond_length_error": 0.0, - "wall_dist_error": 0.0, - "force_flip_fail": 0, + "well_depth_error": 0.0, + "force_flip_count": 1, "model_excluded": False, } record.update(overrides) @@ -64,7 +63,6 @@ def test_scored_set_and_reference_gate(): "Ir", ): assert element not in names - assert low_quality_elements() == frozenset() distances, energies = load_reference()["H"] assert distances[0] == pytest.approx( 0.8 * float(covalent_radii[atomic_numbers["H"]]) @@ -76,11 +74,9 @@ def test_scored_set_and_reference_gate(): def test_reference_curves_have_a_wall_and_an_interior_minimum(): """Every stored PBE curve is a rising short-range wall on a 0.8 r_cov–6 Å grid. - Elements that bind by at least 0.05 eV, and that pass the smoothness - gate, also place that minimum inside the scoring window rather than on - an endpoint. + Elements that bind by at least 0.05 eV also place that minimum inside + the scoring window rather than on an endpoint. """ - low_quality = low_quality_elements() for element, (distances, energies) in load_reference().items(): rcov = float(covalent_radii[atomic_numbers[element]]) assert len(distances) == len(energies) == 50 @@ -89,8 +85,6 @@ def test_reference_curves_have_a_wall_and_an_interior_minimum(): assert distances[0] == pytest.approx(0.8 * rcov) assert distances[-1] == pytest.approx(6.0) assert float(energies[0] - energies.min()) > 1.0 - if element in low_quality: - continue r_min, r_max = eval_window(element, float(distances[-1])) window = (distances >= r_min) & (distances <= r_max) radii = distances[window] @@ -104,11 +98,12 @@ def test_reference_curves_have_a_wall_and_an_interior_minimum(): def test_dummy_scales_are_positive(): dummy = reference_dummy_scales() - assert dummy["bond_length_mae"] == pytest.approx(0.6186, abs=1e-3) - assert dummy["wall_dist_mae"] == pytest.approx(0.2802, abs=1e-3) + assert dummy["bond_length_mae"] == pytest.approx(1.1173, abs=1e-3) + assert dummy["well_depth_mae"] == pytest.approx(2.5920, abs=1e-3) + assert dummy["force_flip_deviation"] == pytest.approx(1.0) -def test_identical_parabola_has_zero_geometry_error(): +def test_identical_parabola_has_zero_reference_error(): element, distances, _r0, energies, force_x = _parabola() metrics = score_curve( element, @@ -116,12 +111,11 @@ def test_identical_parabola_has_zero_geometry_error(): energies, energies, force_x, - reference_low_quality=False, ) assert metrics["model_excluded"] is False assert metrics["bond_length_error"] == pytest.approx(0.0, abs=1e-6) - assert metrics["wall_dist_error"] == pytest.approx(0.0, abs=1e-6) - assert metrics["force_flip_fail"] == 0 + assert metrics["well_depth_error"] == pytest.approx(0.0, abs=1e-6) + assert metrics["force_flip_count"] == 1 def test_shifted_well_moves_bond_length(): @@ -133,34 +127,48 @@ def test_shifted_well_moves_bond_length(): energies, shifted, shifted_fx, - reference_low_quality=False, ) assert metrics["bond_length_error"] == pytest.approx(0.15, abs=1e-6) -def test_unbound_reference_skips_bond_length(): +def test_scaled_well_changes_well_depth(): + element, distances, _r0, energies, force_x = _parabola() + scaled = energies * 0.5 + metrics = score_curve(element, distances, energies, scaled, force_x) + r_min, r_max = eval_window(element, float(distances[-1])) + window = (distances >= r_min) & (distances <= r_max) + ref_depth = float(energies[window][-1] - energies[window].min()) + assert metrics["well_depth_error"] == pytest.approx(0.5 * ref_depth) + + +def test_unbound_reference_skips_geometry_and_well_depth(): element, distances, r0, _energies, force_x = _parabola(scale=1e-4) energies = 1e-4 * (distances - r0) ** 2 - metrics = score_curve( - element, distances, energies, energies, force_x, reference_low_quality=False - ) + metrics = score_curve(element, distances, energies, energies, force_x) assert metrics["model_excluded"] is False assert metrics["bond_length_error"] is None + assert metrics["well_depth_error"] is None -def test_missing_model_wall_uses_reference_radius(): +def test_flat_model_has_zero_depth_and_reference_depth_error(): element, distances, _r0, energies, force_x = _parabola() flat = np.full_like(energies, energies.min()) - metrics = score_curve( - element, distances, energies, flat, force_x, reference_low_quality=False - ) - assert metrics["wall_dist_error"] is not None - assert metrics["wall_dist_error"] > 0.5 + metrics = score_curve(element, distances, energies, flat, force_x) + r_min, r_max = eval_window(element, float(distances[-1])) + window = (distances >= r_min) & (distances <= r_max) + ref_depth = float(energies[window][-1] - energies[window].min()) + assert metrics["well_depth_error"] == pytest.approx(ref_depth) -def test_force_sign_must_change_once(): +def test_force_flip_metric_reports_actual_count(): element, distances, _r0, energies, force_x = _parabola() - oscillating = np.sin(np.linspace(0, 6 * np.pi, len(distances))) + r_min, r_max = eval_window(element, float(distances[-1])) + indices = np.flatnonzero((distances >= r_min) & (distances <= r_max)) + oscillating = np.ones_like(force_x) + third = len(indices) // 3 + oscillating[indices[:third]] = -1 + oscillating[indices[third : 2 * third]] = 1 + oscillating[indices[2 * third :]] = -1 flat = np.zeros_like(force_x) extra = score_curve( element, @@ -168,59 +176,50 @@ def test_force_sign_must_change_once(): energies, energies, oscillating, - reference_low_quality=False, - ) - none = score_curve( - element, distances, energies, energies, flat, reference_low_quality=False ) - assert extra["force_flip_fail"] == 1 - assert none["force_flip_fail"] == 1 + none = score_curve(element, distances, energies, energies, flat) + assert extra["force_flip_count"] == 2 + assert none["force_flip_count"] == 0 assert extra["model_excluded"] is False -def test_low_quality_reference_skips_geometry(): - element, distances, _r0, energies, force_x = _parabola() - metrics = score_curve( - element, distances, energies, energies, force_x, reference_low_quality=True - ) - assert metrics["model_excluded"] is False - assert metrics["bond_length_error"] is None - assert metrics["wall_dist_error"] is None - assert metrics["force_flip_fail"] == 0 - - -def test_nonfinite_and_jumpy_curves_are_excluded(): +def test_nonfinite_curve_is_excluded_but_jumpy_curve_is_scored(): element, distances, _r0, energies, force_x = _parabola() broken = energies.copy() broken[len(broken) // 2] = np.nan - assert score_curve( - element, distances, energies, broken, force_x, reference_low_quality=False - )["model_excluded"] + assert score_curve(element, distances, energies, broken, force_x)["model_excluded"] jumpy = np.zeros_like(energies) jumpy[10:15] += 5.0 jumpy[25:30] -= 5.0 jumpy[40:45] += 5.0 - assert score_curve( - element, distances, energies, jumpy, force_x, reference_low_quality=False - )["model_excluded"] + assert not score_curve(element, distances, energies, jumpy, force_x)[ + "model_excluded" + ] def test_aggregated_perfect_score_is_zero(): agg = aggregated_diatomics_results(_full_results()) assert agg["coverage"] == pytest.approx(1.0) assert agg["bond_length_mae"] == pytest.approx(0.0) - assert agg["wall_dist_mae"] == pytest.approx(0.0) - assert agg["force_flip_rate"] == pytest.approx(0.0) + assert agg["well_depth_mae"] == pytest.approx(0.0) + assert agg["force_flip_count"] == pytest.approx(1.0) assert agg["score"] == pytest.approx(0.0) -def test_aggregated_caps_geometry_at_dummy(): +@pytest.mark.parametrize( + ("metric", "dummy_key"), + ( + ("bond_length_error", "bond_length_mae"), + ("well_depth_error", "well_depth_mae"), + ), +) +def test_aggregated_caps_reference_errors_at_dummy(metric, dummy_key): dummy = reference_dummy_scales() agg = aggregated_diatomics_results( _full_results( **{ - name: _record(bond_length_error=dummy["bond_length_mae"] * 5) + name: _record(**{metric: dummy[dummy_key] * 5}) for name in scored_element_names() } ) @@ -228,14 +227,14 @@ def test_aggregated_caps_geometry_at_dummy(): assert agg["score"] == pytest.approx(1.0 / 3.0) -def test_aggregated_flip_rate_and_coverage(): +def test_aggregated_flip_deviation_and_coverage(): names = scored_element_names() - results = _full_results(**{name: _record(force_flip_fail=1) for name in names}) - results["H"] = _record(model_excluded=True, force_flip_fail=None) + results = _full_results(**{name: _record(force_flip_count=3) for name in names}) + results["H"] = _record(model_excluded=True, force_flip_count=None) agg = aggregated_diatomics_results(results) coverage = (len(names) - 1) / len(names) assert agg["coverage"] == pytest.approx(coverage) - assert agg["force_flip_rate"] == pytest.approx(1.0) + assert agg["force_flip_count"] == pytest.approx(3.0) assert agg["score"] == pytest.approx(1.0 / (3.0 * coverage)) @@ -257,5 +256,5 @@ def test_aggregated_empty_or_incomplete_terms_have_no_score(): **{name: _record(bond_length_error=None) for name in scored_element_names()} ) ) - assert no_bond["force_flip_rate"] == pytest.approx(0.0) + assert no_bond["force_flip_count"] == pytest.approx(1.0) assert no_bond["score"] is None From 09304fc966b842955e87f11924a0e8667e809ff3 Mon Sep 17 00:00:00 2001 From: Anyang Peng <137014849+anyangml@users.noreply.github.com> Date: Wed, 30 Sep 2026 15:43:26 +0800 Subject: [PATCH 7/9] Stabilize Ruff checks after the hook upgrade. Keep the repository's established E/F rule scope explicit and resolve the remaining import-order findings without changing runtime behavior. Co-authored-by: Cursor --- .pre-commit-config.yaml | 2 +- lambench/tasks/calculator/elastic/elastic.py | 2 -- lambench/tasks/utils/__init__.py | 1 - lambench/workflow/dflow.py | 3 +++ 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 55fd4cf..78eae46 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -10,5 +10,5 @@ repos: rev: v0.16.9 hooks: - id: ruff - args: ["--fix"] + args: ["--fix", "--select", "E4,E7,E9,F"] - id: ruff-format diff --git a/lambench/tasks/calculator/elastic/elastic.py b/lambench/tasks/calculator/elastic/elastic.py index 28c08ed..33e891b 100644 --- a/lambench/tasks/calculator/elastic/elastic.py +++ b/lambench/tasks/calculator/elastic/elastic.py @@ -32,9 +32,7 @@ CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" -""" The test data is obtained from the following paper: de Jong, M., Chen, W., Angsten, T. et al. Charting the complete elastic properties of inorganic crystalline compounds. diff --git a/lambench/tasks/utils/__init__.py b/lambench/tasks/utils/__init__.py index 1aa881a..e94735f 100644 --- a/lambench/tasks/utils/__init__.py +++ b/lambench/tasks/utils/__init__.py @@ -1,7 +1,6 @@ import logging from math import isnan from pathlib import Path -from typing import Optional def parse_dptest_log_file( diff --git a/lambench/workflow/dflow.py b/lambench/workflow/dflow.py index dd3681f..7cac3b9 100644 --- a/lambench/workflow/dflow.py +++ b/lambench/workflow/dflow.py @@ -1,3 +1,6 @@ +# Environment variables must be loaded before importing dflow and lambench. +# ruff: noqa: E402 + import logging import os from pathlib import Path From 2ed928fe3d20286b00176c2e9cf3281b6dd6106f Mon Sep 17 00:00:00 2001 From: Anyang Peng <137014849+anyangml@users.noreply.github.com> Date: Wed, 14 Oct 2026 16:47:11 +0800 Subject: [PATCH 8/9] Restore finite-difference diatomic roughness alongside PBE bond metrics --- lambench/metrics/utils.py | 31 ++--- .../tasks/calculator/diatomics/diatomics.py | 123 +++++++++++------- tests/tasks/calculator/test_diatomics.py | 123 +++++++++++------- 3 files changed, 164 insertions(+), 113 deletions(-) diff --git a/lambench/metrics/utils.py b/lambench/metrics/utils.py index 562ba4f..e19e6d3 100644 --- a/lambench/metrics/utils.py +++ b/lambench/metrics/utils.py @@ -158,7 +158,7 @@ def _empty_diatomics_agg() -> dict[str, float | None]: return { "bond_length_mae": None, "well_depth_mae": None, - "force_flip_count": None, + "avg_roughness": None, "coverage": 0.0, "score": None, } @@ -179,17 +179,17 @@ def _dummy_hat(value: float | None, dummy: float) -> float | None: def _element_is_excluded(record: dict | None) -> bool: if not isinstance(record, dict) or record.get("model_excluded", True): return True - flip = record.get("force_flip_count") - return flip is None or not np.isfinite(flip) + roughness = record.get("roughness") + return roughness is None or not np.isfinite(roughness) or not 0 <= roughness <= 1 def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float | None]: """Aggregate per-element diatomic curves into one coverage-weighted score. - bond_length_mae is in Å and well_depth_mae is in eV. force_flip_count is - the mean number of sign changes in first-atom Fx. Bond and well errors are - divided by their blind dummy scales; force flips use the mean per-element - absolute deviation from the ideal count of one. Each term is capped at 1. + bond_length_mae is in Å and well_depth_mae is in eV. avg_roughness is + the mean original finite-difference slope error normalized by a constant + energy dummy. Bond and well errors use their blind dummy scales. Each + term is capped at 1. score is their equal-weight average divided by coverage. Missing elements and model_excluded curves lower coverage instead of discarding the model. score is None when coverage is zero or any term has no finite samples. @@ -205,8 +205,7 @@ def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float | bond_errors: list[float] = [] well_errors: list[float] = [] - flip_counts: list[float] = [] - flip_deviations: list[float] = [] + roughness_values: list[float] = [] n_excluded = 0 for name in names: record = None if not results else results.get(name) @@ -220,27 +219,23 @@ def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float | bond_errors.append(float(bond)) if well is not None and np.isfinite(well): well_errors.append(float(well)) - flip_count = float(record["force_flip_count"]) - flip_counts.append(flip_count) - flip_deviations.append(abs(flip_count - 1.0)) + roughness_values.append(float(record["roughness"])) coverage = (len(names) - n_excluded) / len(names) bond_mae = _mean_or_none(bond_errors) well_mae = _mean_or_none(well_errors) - flip_count = _mean_or_none(flip_counts) - flip_deviation = _mean_or_none(flip_deviations) + avg_roughness = _mean_or_none(roughness_values) dummy = reference_dummy_scales() bond_hat = _dummy_hat(bond_mae, dummy["bond_length_mae"]) well_hat = _dummy_hat(well_mae, dummy["well_depth_mae"]) - flip_hat = _dummy_hat(flip_deviation, dummy["force_flip_deviation"]) - if coverage <= 0 or bond_hat is None or well_hat is None or flip_hat is None: + if coverage <= 0 or bond_hat is None or well_hat is None or avg_roughness is None: score = None else: - score = float((bond_hat + well_hat + flip_hat) / (3.0 * coverage)) + score = float((bond_hat + well_hat + avg_roughness) / (3.0 * coverage)) return { "bond_length_mae": bond_mae, "well_depth_mae": well_mae, - "force_flip_count": flip_count, + "avg_roughness": avg_roughness, "coverage": float(coverage), "score": score, } diff --git a/lambench/tasks/calculator/diatomics/diatomics.py b/lambench/tasks/calculator/diatomics/diatomics.py index 3368de3..38f734d 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.py +++ b/lambench/tasks/calculator/diatomics/diatomics.py @@ -36,17 +36,20 @@ - well_depth_error: absolute error in eV of ``E(r_far) - min(E)`` over the evaluation window. It is skipped with bond length when the reference binding energy is below 0.05 eV. -- force_flip_count: number of Fx sign changes on the first atom after - dropping |Fx| < 0.01 eV/Å. +- roughness: finite-difference slope MAE between independently min-shifted, + peak-normalized model and reference energies, normalized by a constant-energy + dummy and capped at one. Uses the native grid with the last point dropped. -All metrics use distances from 0.9 covalent radii to -min(3.1 Alvarez vdW radii, 6 Å). A model curve that is non-finite in that -range is dropped from the averages and lowers coverage. +Bond length and well depth use distances from 0.9 covalent radii to +min(3.1 Alvarez vdW radii, 6 Å). Roughness uses the native reference grid +with its last point dropped, preserving the original finite-difference metric. +A curve with non-finite values in either scored range is dropped from the +averages and lowers coverage. The leaderboard score is the equal-weight average of the dummy-normalized -bond-length MAE, dummy-normalized well-depth MAE, and mean absolute -deviation from one force flip, divided by coverage. The bond-length dummy -guesses the midpoint of each element's evaluation window. The well-depth +bond-length MAE, dummy-normalized well-depth MAE, and mean roughness, +divided by coverage. The bond-length dummy guesses the midpoint of each +element's evaluation window. The well-depth dummy predicts zero binding energy. Zero is a perfect single-well match. Reference file: lambench/tasks/calculator/diatomics/diatomics.json @@ -73,7 +76,7 @@ _GRID_R_MAX = 6.0 _MIN_BINDING_EV = 0.05 _MIN_WINDOW_POINTS = 5 -_FORCE_SIGN_THRESHOLD = 1e-2 +_DROP_LAST_POINTS = 1 _FIT_POINTS = 5 _BOX_ANGSTROM = 30.0 @@ -89,8 +92,8 @@ def eval_window( ) -> tuple[float, float]: """Element-specific scoring window in Å. - The default lower bound is 0.9 covalent radii. Wall scoring passes - ``r_min_factor=0.8``. The upper bound is min(3.1 Alvarez vdW radii, + The default lower bound is 0.9 covalent radii. The upper bound is + min(3.1 Alvarez vdW radii, ``seps_max``). Missing vdW radii leave the upper bound at ``seps_max``. """ atomic_num = atomic_numbers[element] @@ -124,18 +127,46 @@ def _equilibrium_distance(seps: np.ndarray, energies: np.ndarray) -> float: return float(seps[min_idx]) -def _force_flip_count(force_x: np.ndarray) -> int: - kept = force_x[np.abs(force_x) >= _FORCE_SIGN_THRESHOLD] - if kept.size < 2: - return 0 - return int(np.sum(np.diff(np.sign(kept)) != 0)) +def _shift_to_min(energies: np.ndarray) -> np.ndarray: + return energies - np.min(energies) -def _excluded_record() -> dict[str, float | int | bool | None]: +def _normalized_slopes(bond_lengths: np.ndarray, y: np.ndarray) -> np.ndarray: + """d(E / max(E)) / dr on the native DFT scan (Å⁻¹).""" + peak = float(np.max(y)) + slopes = np.diff(y) / np.diff(bond_lengths) + if peak > 0: + return slopes / peak + return np.zeros_like(slopes) + + +def _roughness( + bond_lengths: np.ndarray, model_energies: np.ndarray, dft_energies: np.ndarray +) -> float | None: + """Original slope MAE relative to a constant-energy dummy, capped at one. + + Inputs use the native reference grid with the final point already dropped. + The caller validates array shapes and strictly increasing distances. + """ + if not np.all(np.isfinite(model_energies)) or not np.all(np.isfinite(dft_energies)): + return None + if bond_lengths.size < 2: + return None + ref_slopes = _normalized_slopes(bond_lengths, _shift_to_min(dft_energies)) + model_slopes = _normalized_slopes(bond_lengths, _shift_to_min(model_energies)) + # A constant-energy dummy has zero normalized slopes. + dummy_error = float(np.mean(np.abs(ref_slopes))) + error = float(np.mean(np.abs(model_slopes - ref_slopes))) + if dummy_error <= 0 or not np.isfinite(dummy_error) or not np.isfinite(error): + return None + return float(min(error / dummy_error, 1.0)) + + +def _excluded_record() -> dict[str, float | bool | None]: return { "bond_length_error": None, "well_depth_error": None, - "force_flip_count": None, + "roughness": None, "model_excluded": True, } @@ -145,8 +176,7 @@ def score_curve( distances: np.ndarray, ref_energies: np.ndarray, model_energies: np.ndarray, - model_force_x: np.ndarray, -) -> dict[str, float | int | bool | None]: +) -> dict[str, float | bool | None]: """Score one homonuclear curve against its PBE reference. ``model_excluded`` is true when the model curve cannot be scored. @@ -154,12 +184,15 @@ def score_curve( distances = np.asarray(distances, dtype=float) ref_energies = np.asarray(ref_energies, dtype=float) model_energies = np.asarray(model_energies, dtype=float) - model_force_x = np.asarray(model_force_x, dtype=float) - if not ( - distances.size == ref_energies.size == model_energies.size == model_force_x.size - ): + if any(values.ndim != 1 for values in (distances, ref_energies, model_energies)): + return _excluded_record() + if not (distances.size == ref_energies.size == model_energies.size): return _excluded_record() - if distances.size < 2 or np.any(np.diff(distances) <= 0): + if ( + distances.size < 2 + or not np.isfinite(distances).all() + or np.any(np.diff(distances) <= 0) + ): return _excluded_record() seps_max = float(distances[-1]) @@ -169,17 +202,21 @@ def score_curve( return _excluded_record() if not ( np.isfinite(model_energies[general]).all() - and np.isfinite(model_force_x[general]).all() + and np.isfinite(ref_energies[general]).all() ): return _excluded_record() - if not np.isfinite(ref_energies[general]).all(): - return _excluded_record() - n_flips = _force_flip_count(model_force_x[general]) - result: dict[str, float | int | bool | None] = { + roughness = _roughness( + distances[:-_DROP_LAST_POINTS], + model_energies[:-_DROP_LAST_POINTS], + ref_energies[:-_DROP_LAST_POINTS], + ) + if roughness is None: + return _excluded_record() + result: dict[str, float | bool | None] = { "bond_length_error": None, "well_depth_error": None, - "force_flip_count": n_flips, + "roughness": roughness, "model_excluded": False, } @@ -221,7 +258,6 @@ def reference_dummy_scales(path: str | None = None) -> dict[str, float]: For each bound reference, the bond dummy guesses the arithmetic midpoint of that element's evaluation window and the well-depth dummy predicts zero. - A flat force curve has zero flips, one away from the ideal count of one. """ bond_errors: list[float] = [] well_errors: list[float] = [] @@ -229,7 +265,7 @@ def reference_dummy_scales(path: str | None = None) -> dict[str, float]: seps_max = float(np.max(distances)) r_min, r_max = eval_window(element, seps_max) general = (distances >= r_min) & (distances <= r_max) - if int(general.sum()) < 3: + if int(general.sum()) < _MIN_WINDOW_POINTS: continue ref_energies = energies[general] ref_depth = _binding_energy(ref_energies) @@ -242,16 +278,12 @@ def reference_dummy_scales(path: str | None = None) -> dict[str, float]: return { "bond_length_mae": float(np.mean(bond_errors)), "well_depth_mae": float(np.mean(well_errors)), - "force_flip_deviation": 1.0, } -def _predict_curve( - calc: Calculator, element: str, distances: np.ndarray -) -> tuple[np.ndarray, np.ndarray]: - """Model energy (eV) and Fx on the first atom (eV/Å) at each distance.""" +def _predict_curve(calc: Calculator, element: str, distances: np.ndarray) -> np.ndarray: + """Model energy (eV) at each distance.""" energies = np.empty(distances.size, dtype=float) - force_x = np.empty(distances.size, dtype=float) for i, distance in enumerate(distances): atoms = Atoms( symbols=[element, element], @@ -262,17 +294,13 @@ def _predict_curve( atoms.calc = calc try: energy = float(atoms.get_potential_energy()) - forces = np.asarray(atoms.get_forces(), dtype=float) - fx = float(forces[0, 0]) - if not np.isfinite(energy) or not np.isfinite(forces).all(): - raise ValueError("non-finite energy or forces") + if not np.isfinite(energy): + raise ValueError("non-finite energy") except Exception as exc: logging.warning(f"{element}2 @ r={float(distance):.3f} Å failed: {exc}") energy = np.nan - fx = np.nan energies[i] = energy - force_x[i] = fx - return energies, force_x + return energies def run_inference(model: ASEModel, test_data: Path | None = None) -> dict[str, dict]: @@ -282,13 +310,12 @@ def run_inference(model: ASEModel, test_data: Path | None = None) -> dict[str, d calc = model.calc results: dict[str, dict] = {} for element, (distances, ref_energies) in reference.items(): - model_energies, model_force_x = _predict_curve(calc, element, distances) + model_energies = _predict_curve(calc, element, distances) element_result = score_curve( element, distances, ref_energies, model_energies, - model_force_x, ) results[element] = element_result logging.info(f"{element}: {element_result}") diff --git a/tests/tasks/calculator/test_diatomics.py b/tests/tasks/calculator/test_diatomics.py index 025c7ad..d4b9a19 100644 --- a/tests/tasks/calculator/test_diatomics.py +++ b/tests/tasks/calculator/test_diatomics.py @@ -18,15 +18,14 @@ def _parabola(element: str = "Si", shift: float = 0.0, scale: float = 80.0): distances = np.linspace(r_lo + 1e-4, r_hi - 1e-4, 60) r0 = distances[int(len(distances) * 0.65)] energies = scale * (distances - (r0 + shift)) ** 2 - force_x = distances - (r0 + shift) - return element, distances, r0, energies, force_x + return element, distances, r0, energies def _record(**overrides): record = { "bond_length_error": 0.0, "well_depth_error": 0.0, - "force_flip_count": 1, + "roughness": 0.0, "model_excluded": False, } record.update(overrides) @@ -100,41 +99,38 @@ def test_dummy_scales_are_positive(): dummy = reference_dummy_scales() assert dummy["bond_length_mae"] == pytest.approx(1.1173, abs=1e-3) assert dummy["well_depth_mae"] == pytest.approx(2.5920, abs=1e-3) - assert dummy["force_flip_deviation"] == pytest.approx(1.0) def test_identical_parabola_has_zero_reference_error(): - element, distances, _r0, energies, force_x = _parabola() + element, distances, _r0, energies = _parabola() metrics = score_curve( element, distances, energies, energies, - force_x, ) assert metrics["model_excluded"] is False assert metrics["bond_length_error"] == pytest.approx(0.0, abs=1e-6) assert metrics["well_depth_error"] == pytest.approx(0.0, abs=1e-6) - assert metrics["force_flip_count"] == 1 + assert metrics["roughness"] == pytest.approx(0.0) def test_shifted_well_moves_bond_length(): - element, distances, _r0, energies, force_x = _parabola() - _element, _distances, _shifted_r0, shifted, shifted_fx = _parabola(shift=0.15) + element, distances, _r0, energies = _parabola() + _element, _distances, _shifted_r0, shifted = _parabola(shift=0.15) metrics = score_curve( element, distances, energies, shifted, - shifted_fx, ) assert metrics["bond_length_error"] == pytest.approx(0.15, abs=1e-6) def test_scaled_well_changes_well_depth(): - element, distances, _r0, energies, force_x = _parabola() + element, distances, _r0, energies = _parabola() scaled = energies * 0.5 - metrics = score_curve(element, distances, energies, scaled, force_x) + metrics = score_curve(element, distances, energies, scaled) r_min, r_max = eval_window(element, float(distances[-1])) window = (distances >= r_min) & (distances <= r_max) ref_depth = float(energies[window][-1] - energies[window].min()) @@ -142,60 +138,93 @@ def test_scaled_well_changes_well_depth(): def test_unbound_reference_skips_geometry_and_well_depth(): - element, distances, r0, _energies, force_x = _parabola(scale=1e-4) + element, distances, r0, _energies = _parabola(scale=1e-4) energies = 1e-4 * (distances - r0) ** 2 - metrics = score_curve(element, distances, energies, energies, force_x) + metrics = score_curve(element, distances, energies, energies) assert metrics["model_excluded"] is False assert metrics["bond_length_error"] is None assert metrics["well_depth_error"] is None def test_flat_model_has_zero_depth_and_reference_depth_error(): - element, distances, _r0, energies, force_x = _parabola() + element, distances, _r0, energies = _parabola() flat = np.full_like(energies, energies.min()) - metrics = score_curve(element, distances, energies, flat, force_x) + metrics = score_curve(element, distances, energies, flat) r_min, r_max = eval_window(element, float(distances[-1])) window = (distances >= r_min) & (distances <= r_max) ref_depth = float(energies[window][-1] - energies[window].min()) assert metrics["well_depth_error"] == pytest.approx(ref_depth) -def test_force_flip_metric_reports_actual_count(): - element, distances, _r0, energies, force_x = _parabola() - r_min, r_max = eval_window(element, float(distances[-1])) - indices = np.flatnonzero((distances >= r_min) & (distances <= r_max)) - oscillating = np.ones_like(force_x) - third = len(indices) // 3 - oscillating[indices[:third]] = -1 - oscillating[indices[third : 2 * third]] = 1 - oscillating[indices[2 * third :]] = -1 - flat = np.zeros_like(force_x) - extra = score_curve( - element, - distances, - energies, - energies, - oscillating, +def test_roughness_matches_original_finite_difference_formula(): + element, distances, _r0, energies = _parabola() + predicted = energies.copy() + predicted[20:25] += 15.0 + r = distances[:-1] + ref = energies[:-1] - energies[:-1].min() + pred = predicted[:-1] - predicted[:-1].min() + ref_slopes = np.diff(ref) / np.diff(r) / ref.max() + pred_slopes = np.diff(pred) / np.diff(r) / pred.max() + expected = min( + np.mean(np.abs(pred_slopes - ref_slopes)) / np.mean(np.abs(ref_slopes)), + 1.0, ) - none = score_curve(element, distances, energies, energies, flat) - assert extra["force_flip_count"] == 2 - assert none["force_flip_count"] == 0 - assert extra["model_excluded"] is False + result = score_curve(element, distances, energies, predicted) + assert result["roughness"] == pytest.approx(expected) + assert result["roughness"] > 0 + + +def test_roughness_flat_dummy_and_energy_scale_invariance(): + element, distances, _r0, energies = _parabola() + flat = score_curve(element, distances, energies, np.zeros_like(energies)) + scaled = score_curve(element, distances, energies, energies * 7 + 10) + assert flat["roughness"] == pytest.approx(1.0) + assert scaled["roughness"] == pytest.approx(0.0, abs=1e-12) + + +def test_roughness_drops_last_point(): + element, distances, _r0, energies = _parabola() + changed = energies.copy() + changed[-1] += 100 + result = score_curve(element, distances, energies, changed) + assert result["roughness"] == pytest.approx(0.0) def test_nonfinite_curve_is_excluded_but_jumpy_curve_is_scored(): - element, distances, _r0, energies, force_x = _parabola() + element, distances, _r0, energies = _parabola() broken = energies.copy() broken[len(broken) // 2] = np.nan - assert score_curve(element, distances, energies, broken, force_x)["model_excluded"] + assert score_curve(element, distances, energies, broken)["model_excluded"] jumpy = np.zeros_like(energies) jumpy[10:15] += 5.0 jumpy[25:30] -= 5.0 jumpy[40:45] += 5.0 - assert not score_curve(element, distances, energies, jumpy, force_x)[ - "model_excluded" - ] + assert not score_curve(element, distances, energies, jumpy)["model_excluded"] + + +@pytest.mark.parametrize( + "invalid", ["nonfinite", "duplicate", "reversed", "matrix", "length"] +) +def test_invalid_curve_grid_is_excluded(invalid): + element, distances, _r0, energies = _parabola() + if invalid == "nonfinite": + distances[10] = np.nan + elif invalid == "duplicate": + distances[10] = distances[9] + elif invalid == "reversed": + distances = distances[::-1] + elif invalid == "matrix": + distances = distances.reshape(-1, 1) + else: + distances = distances[:-1] + assert score_curve(element, distances, energies, energies)["model_excluded"] + + +def test_constant_reference_has_no_roughness_baseline(): + element, distances, _r0, energies = _parabola() + flat = np.zeros_like(energies) + assert score_curve(element, distances, flat, energies)["model_excluded"] def test_aggregated_perfect_score_is_zero(): @@ -203,7 +232,7 @@ def test_aggregated_perfect_score_is_zero(): assert agg["coverage"] == pytest.approx(1.0) assert agg["bond_length_mae"] == pytest.approx(0.0) assert agg["well_depth_mae"] == pytest.approx(0.0) - assert agg["force_flip_count"] == pytest.approx(1.0) + assert agg["avg_roughness"] == pytest.approx(0.0) assert agg["score"] == pytest.approx(0.0) @@ -227,14 +256,14 @@ def test_aggregated_caps_reference_errors_at_dummy(metric, dummy_key): assert agg["score"] == pytest.approx(1.0 / 3.0) -def test_aggregated_flip_deviation_and_coverage(): +def test_aggregated_roughness_and_coverage(): names = scored_element_names() - results = _full_results(**{name: _record(force_flip_count=3) for name in names}) - results["H"] = _record(model_excluded=True, force_flip_count=None) + results = _full_results(**{name: _record(roughness=1.0) for name in names}) + results["H"] = _record(model_excluded=True, roughness=None) agg = aggregated_diatomics_results(results) coverage = (len(names) - 1) / len(names) assert agg["coverage"] == pytest.approx(coverage) - assert agg["force_flip_count"] == pytest.approx(3.0) + assert agg["avg_roughness"] == pytest.approx(1.0) assert agg["score"] == pytest.approx(1.0 / (3.0 * coverage)) @@ -256,5 +285,5 @@ def test_aggregated_empty_or_incomplete_terms_have_no_score(): **{name: _record(bond_length_error=None) for name in scored_element_names()} ) ) - assert no_bond["force_flip_count"] == pytest.approx(1.0) + assert no_bond["avg_roughness"] == pytest.approx(0.0) assert no_bond["score"] is None From 20340263555bb687ec1579c68b4d451b4ad4dd27 Mon Sep 17 00:00:00 2001 From: Anyang Peng <137014849+anyangml@users.noreply.github.com> Date: Wed, 14 Oct 2026 17:02:48 +0800 Subject: [PATCH 9/9] Require complete diatomic coverage and persist reference baselines --- lambench/metrics/utils.py | 38 +++++---- .../metrics/vishelper/metrics_calculations.py | 2 +- .../tasks/calculator/diatomics/diatomics.py | 20 +++-- tests/tasks/calculator/test_diatomics.py | 79 +++++++++++++++++-- 4 files changed, 109 insertions(+), 30 deletions(-) diff --git a/lambench/metrics/utils.py b/lambench/metrics/utils.py index e19e6d3..aeed564 100644 --- a/lambench/metrics/utils.py +++ b/lambench/metrics/utils.py @@ -170,8 +170,14 @@ def _mean_or_none(values: list[float]) -> float | None: return float(np.mean(values)) -def _dummy_hat(value: float | None, dummy: float) -> float | None: - if value is None or not np.isfinite(value) or not np.isfinite(dummy) or dummy <= 0: +def _dummy_hat(value: float | None, dummy: float | None) -> float | None: + if ( + value is None + or dummy is None + or not np.isfinite(value) + or not np.isfinite(dummy) + or dummy <= 0 + ): return None return float(min(value / dummy, 1.0)) @@ -184,22 +190,23 @@ def _element_is_excluded(record: dict | None) -> bool: def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float | None]: - """Aggregate per-element diatomic curves into one coverage-weighted score. + """Aggregate curves using the reference metadata saved during inference. bond_length_mae is in Å and well_depth_mae is in eV. avg_roughness is the mean original finite-difference slope error normalized by a constant energy dummy. Bond and well errors use their blind dummy scales. Each term is capped at 1. - score is their equal-weight average divided by coverage. Missing elements - and model_excluded curves lower coverage instead of discarding the model. - score is None when coverage is zero or any term has no finite samples. + score is their equal-weight average, reported only for complete coverage. + Missing or model_excluded elements make score None. Raw metrics and + coverage are retained for diagnostics. A term without samples also makes + score None (for example, a reference set with no bound elements). """ - from lambench.tasks.calculator.diatomics.diatomics import ( - reference_dummy_scales, - scored_element_names, - ) - - names = scored_element_names() + if not results: + return _empty_diatomics_agg() + reference = results["reference"] + names = reference["elements"] + dummy = reference["dummy_scales"] + curves = results["curves"] if not names: return _empty_diatomics_agg() @@ -208,7 +215,7 @@ def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float | roughness_values: list[float] = [] n_excluded = 0 for name in names: - record = None if not results else results.get(name) + record = curves.get(name) if _element_is_excluded(record): n_excluded += 1 continue @@ -225,13 +232,12 @@ def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float | bond_mae = _mean_or_none(bond_errors) well_mae = _mean_or_none(well_errors) avg_roughness = _mean_or_none(roughness_values) - dummy = reference_dummy_scales() bond_hat = _dummy_hat(bond_mae, dummy["bond_length_mae"]) well_hat = _dummy_hat(well_mae, dummy["well_depth_mae"]) - if coverage <= 0 or bond_hat is None or well_hat is None or avg_roughness is None: + if n_excluded or bond_hat is None or well_hat is None or avg_roughness is None: score = None else: - score = float((bond_hat + well_hat + avg_roughness) / (3.0 * coverage)) + score = float((bond_hat + well_hat + avg_roughness) / 3.0) return { "bond_length_mae": bond_mae, "well_depth_mae": well_mae, diff --git a/lambench/metrics/vishelper/metrics_calculations.py b/lambench/metrics/vishelper/metrics_calculations.py index c9d11f7..7722b2d 100644 --- a/lambench/metrics/vishelper/metrics_calculations.py +++ b/lambench/metrics/vishelper/metrics_calculations.py @@ -201,7 +201,7 @@ def _calculate_instability_error(self, cell: dict, lambda_0: float = 5e-4) -> fl def calculate_diatomics_results(self) -> dict[str, float]: """ - Leaderboard scores for homonuclear diatomics: coverage-weighted score + Leaderboard scores for homonuclear diatomics: complete-coverage score (lower is better). Models with missing results are omitted from this dict; the final ranking still includes them with a null column. """ diff --git a/lambench/tasks/calculator/diatomics/diatomics.py b/lambench/tasks/calculator/diatomics/diatomics.py index 38f734d..c85dd9e 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.py +++ b/lambench/tasks/calculator/diatomics/diatomics.py @@ -48,8 +48,8 @@ The leaderboard score is the equal-weight average of the dummy-normalized bond-length MAE, dummy-normalized well-depth MAE, and mean roughness, -divided by coverage. The bond-length dummy guesses the midpoint of each -element's evaluation window. The well-depth +and is only reported when every reference element is scored. The bond-length +dummy guesses the midpoint of each element's evaluation window. The well-depth dummy predicts zero binding energy. Zero is a perfect single-well match. Reference file: lambench/tasks/calculator/diatomics/diatomics.json @@ -253,7 +253,7 @@ def scored_element_names(path: str | None = None) -> tuple[str, ...]: @lru_cache -def reference_dummy_scales(path: str | None = None) -> dict[str, float]: +def reference_dummy_scales(path: str | None = None) -> dict[str, float | None]: """Metric scales from blind bond-position and flat-energy predictions. For each bound reference, the bond dummy guesses the arithmetic midpoint @@ -276,8 +276,8 @@ def reference_dummy_scales(path: str | None = None) -> dict[str, float]: well_errors.append(ref_depth) return { - "bond_length_mae": float(np.mean(bond_errors)), - "well_depth_mae": float(np.mean(well_errors)), + "bond_length_mae": float(np.mean(bond_errors)) if bond_errors else None, + "well_depth_mae": float(np.mean(well_errors)) if well_errors else None, } @@ -304,7 +304,7 @@ def _predict_curve(calc: Calculator, element: str, distances: np.ndarray) -> np. def run_inference(model: ASEModel, test_data: Path | None = None) -> dict[str, dict]: - """Score model curves on the PBE homonuclear reference grid.""" + """Score curves and persist their reference set and dummy normalization.""" label_path = None if test_data is None else str(test_data / "diatomics.json") reference = load_reference(label_path) calc = model.calc @@ -319,4 +319,10 @@ def run_inference(model: ASEModel, test_data: Path | None = None) -> dict[str, d ) results[element] = element_result logging.info(f"{element}: {element_result}") - return results + return { + "reference": { + "elements": list(reference), + "dummy_scales": reference_dummy_scales(label_path), + }, + "curves": results, + } diff --git a/tests/tasks/calculator/test_diatomics.py b/tests/tasks/calculator/test_diatomics.py index d4b9a19..b5e565e 100644 --- a/tests/tasks/calculator/test_diatomics.py +++ b/tests/tasks/calculator/test_diatomics.py @@ -1,3 +1,6 @@ +import json +from types import SimpleNamespace + import numpy as np import pytest from ase.data import atomic_numbers, covalent_radii @@ -8,6 +11,7 @@ eval_window, load_reference, reference_dummy_scales, + run_inference, score_curve, scored_element_names, ) @@ -35,7 +39,13 @@ def _record(**overrides): def _full_results(**overrides): results = {name: _record() for name in scored_element_names()} results.update(overrides) - return results + return { + "reference": { + "elements": list(scored_element_names()), + "dummy_scales": reference_dummy_scales(), + }, + "curves": results, + } def test_scored_set_and_reference_gate(): @@ -259,20 +269,20 @@ def test_aggregated_caps_reference_errors_at_dummy(metric, dummy_key): def test_aggregated_roughness_and_coverage(): names = scored_element_names() results = _full_results(**{name: _record(roughness=1.0) for name in names}) - results["H"] = _record(model_excluded=True, roughness=None) + results["curves"]["H"] = _record(model_excluded=True, roughness=None) agg = aggregated_diatomics_results(results) coverage = (len(names) - 1) / len(names) assert agg["coverage"] == pytest.approx(coverage) assert agg["avg_roughness"] == pytest.approx(1.0) - assert agg["score"] == pytest.approx(1.0 / (3.0 * coverage)) + assert agg["score"] is None def test_aggregated_missing_element_counts_against_coverage(): results = _full_results() - del results["H"] + del results["curves"]["H"] agg = aggregated_diatomics_results(results) - assert agg["coverage"] == pytest.approx((len(results)) / (len(results) + 1)) - assert agg["score"] == pytest.approx(0.0) + assert agg["coverage"] == pytest.approx(75 / 76) + assert agg["score"] is None def test_aggregated_empty_or_incomplete_terms_have_no_score(): @@ -287,3 +297,60 @@ def test_aggregated_empty_or_incomplete_terms_have_no_score(): ) assert no_bond["avg_roughness"] == pytest.approx(0.0) assert no_bond["score"] is None + + +def test_custom_reference_metadata_survives_json_roundtrip(tmp_path, monkeypatch): + reference = { + name: (r, e * 3 + 10) + for name, (r, e) in load_reference().items() + if name in {"H", "Li"} + } + path = tmp_path / "diatomics.json" + path.write_text( + json.dumps( + [ + {"element": name, "R": r.tolist(), "E": e.tolist()} + for name, (r, e) in reference.items() + ] + ) + ) + monkeypatch.setattr( + "lambench.tasks.calculator.diatomics.diatomics._predict_curve", + lambda calc, name, distances: reference[name][1], + ) + results = json.loads( + json.dumps(run_inference(SimpleNamespace(calc=None), tmp_path)) + ) + dummy = reference_dummy_scales(str(path)) + assert results["reference"]["elements"] == ["H", "Li"] + assert results["reference"]["dummy_scales"] == dummy + assert dummy["well_depth_mae"] != reference_dummy_scales()["well_depth_mae"] + agg = aggregated_diatomics_results(results) + assert agg["coverage"] == 1.0 + assert agg["score"] == pytest.approx(0.0) + + for record in results["curves"].values(): + record.update( + roughness=1.0, + bond_length_error=dummy["bond_length_mae"], + well_depth_error=dummy["well_depth_mae"], + ) + assert aggregated_diatomics_results(results)["score"] == pytest.approx(1.0) + del results["curves"]["Li"] + assert aggregated_diatomics_results(results)["coverage"] == 0.5 + assert aggregated_diatomics_results(results)["score"] is None + + +def test_reference_without_bound_elements_has_no_dummy_scale(tmp_path): + r, e = load_reference()["He"] + path = tmp_path / "diatomics.json" + path.write_text(json.dumps([{"element": "He", "R": r.tolist(), "E": e.tolist()}])) + dummy = reference_dummy_scales(str(path)) + assert dummy == {"bond_length_mae": None, "well_depth_mae": None} + results = { + "reference": {"elements": ["He"], "dummy_scales": dummy}, + "curves": {"He": score_curve("He", r, e, e)}, + } + agg = aggregated_diatomics_results(results) + assert agg["coverage"] == 1.0 + assert agg["score"] is None