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Triaging hypothetical crystals for stability

Classify 215,488 hypothetical crystals as stable or not against a fixed DFT convex hull, scored by F1.

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State the problem, from Matbench Discovery

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	1	+# Triaging hypothetical crystals for stability
	2	+
	3	+High-throughput materials search invents far more candidate crystals than anyone can afford to validate with
	4	+density functional theory. This benchmark asks a model to look at more than 200,000 unrelaxed hypothetical structures
	5	+and say which ones are thermodynamically stable, so that the expensive calculations can be spent on the right ones.
	6	+
	7	+## Task
	8	+
	9	+For each structure in the WBM test set, predict its energy and classify it as stable, meaning it lies on or below
	10	+the Materials Project convex hull, or unstable. The hull is fixed: it is built from the Materials Project's own
	11	+DFT entries as queried on 2023-02-07 (release v2022.10.28). Models may use any training data; the site's
	12	+"Compliant models" filter shows only open models trained exclusively on Materials Project-anchored data.
	13	+The headline score is the $F_1$ of the stable class, the harmonic mean of precision and recall. Most entries are
	14	+machine-learning force fields that relax each structure first and then read off its energy, the protocol the site
	15	+labels IS2RE-SR.
	16	+
	17	+## Background
	18	+
	19	+The test structures come from Wang, Botti and Marques, who generated 257,487 candidates by repeated
	20	+chemically-similar element substitution on Materials Project crystals, in five rounds, and relaxed them all with
	21	+Materials Project-compatible DFT settings. Riebesell and co-workers turned that into a benchmark in 2023: because
	22	+the substitutions move progressively away from the training distribution, the set measures extrapolation rather
	23	+than interpolation, and because the metric is a classification score it reflects what a screening campaign
	24	+actually cares about, which is the hit rate among the candidates it chooses to compute.
	25	+
	26	+## Data and evaluation
	27	+
	28	+- Data: the WBM test set and the Materials Project reference energies, as registered files on figshare
	29	+ (<https://figshare.com/articles/dataset/22715158>) and fetched by the `matbench_discovery` package. After
	30	+ cleaning, 256,963 structures remain of the 257,487 generated.
	31	+- Split: there is no cross-validation; the whole WBM set is the test set. The default leaderboard view is the unique-prototype subset, 215,488 structures
	32	+ left after removing prototypes that already occur in the Materials Project and keeping only the lowest-energy
	33	+ structure per prototype; the full set is the other view.
	34	+- Metric: $F_1$ for the stable class, higher is better. The site also reports discovery acceleration factor,
	35	+ precision, recall, energy MAE and $R^2$, and a combined score CPS that weights $F_1$ at 50%, phonon
	36	+ $\kappa_{\mathrm{SRME}}$ at 40% and geometry-optimisation RMSD at 10%.
	37	+- Official evaluation code: <https://github.com/janosh/matbench-discovery> (MIT); a model is submitted as a pull
	38	+ request with its predictions and a metadata file. The data files on figshare are CC BY 4.0.
	39	+
	40	+## What is known
	41	+
	42	+- Top of the leaderboard on unique prototypes: EquiformerV3+DeNS-OAM at $F_1 = 0.931$, with TECE-OAM-RRA-1.0 and
	43	+ EquFlashV2 at 0.929 and Prophet-OAME-MBD at 0.928. The four are within 0.003 of each other.
	44	+- Prophet-OAME-MBD leads the combined score at CPS 0.912, mostly on the strength of its phonon term.
	45	+- The first generation of universal potentials is far behind: MACE-MP-0 at 0.669, CHGNet at 0.613 and M3GNet
	46	+ at 0.569.
	47	+ Much of the gain since then came from training data: the same architecture trained on OMat24 and Alexandria
	48	+ gains sharply over its MPtrj-only variant, for example eSEN-30M at 0.925 against 0.831.
	49	+- The best compliant model, EquiformerV3+DeNS-MP, is trained only on MPtrj and reaches $F_1 = 0.863$, so part of
	50	+ the gap at the top reflects training data rather than method.
	51	+
	52	+## What would count as progress
	53	+
	54	+- An $F_1$ above 0.931 on the unique-prototype set under the official protocol, submitted with the prediction
	55	+ files so precision, recall and the energy metrics can be recomputed.
	56	+- A compliant model above 0.863, trained only on Materials Project-anchored data, which isolates the method from
	57	+ the data.
	58	+- Code and a checkpoint that reproduce the run, plus the geometry and phonon metrics, since a model that wins on
	59	+ $F_1$ alone can still fail the combined score.
	60	+
	61	+## References
	62	+
	63	+1. J. Riebesell, R. E. A. Goodall, P. Benner et al., "A framework to evaluate machine learning crystal stability
	64	+ predictions", Nature Machine Intelligence 7, 836–847 (2025). <https://doi.org/10.1038/s42256-025-01055-1>
	65	+2. H.-C. Wang, S. Botti and M. A. L. Marques, "Predicting stable crystalline compounds using chemical
	66	+ similarity", npj Computational Materials 7, 12 (2021). <https://doi.org/10.1038/s41524-020-00481-6>
	67	+3. A. Jain et al., "The Materials Project: A materials genome approach to accelerating materials innovation",
	68	+ APL Materials 1, 011002 (2013). <https://doi.org/10.1063/1.4812323>
	69	+4. B. Póta, P. Ahlawat, G. Csányi and M. Simoncelli, "Thermal Conductivity Predictions with Foundation Atomistic
	70	+ Models" (2024), the source of the $\kappa_{\mathrm{SRME}}$ term in CPS. <https://arxiv.org/abs/2408.00755>
	71	+
	72	+## Source
	73	+
	74	+Imported from [Matbench Discovery, crystal stability](https://matbench-discovery.materialsproject.org/benchmarks/discovery), curated by Janosh Riebesell et al. Open according
	75	+to the source as of 2026-10-20. The statement above is written in our own words; see the source for the original
	76	+wording, history and discussion.