Predicting DFT band gaps from crystal structure

Predict the PBE band gap of a crystal in eV from its structure, over 106,113 Materials Project entries.

shoal State the problem, from Matbench 60e7557 · 2周前历史

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1	# Predicting DFT band gaps from crystal structure
2
3	Band gap decides whether a crystal is useful as a semiconductor, a transparent conductor or a photocatalyst, and computing it costs a DFT run per material. This task asks for the computed gap from the structure, on 106,113 Materials Project entries.
4
5	## Task
6
7	Given a crystal structure, predict the PBE band gap in electronvolts as reported by the Materials Project. Scoring is the mean absolute error over the five fixed folds. More than 40% of the entries are metals with zero gap, so the target is a spike at zero plus a broad tail.
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9	## Background
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11	The labels are PBE gaps from the Materials Project, filtered by Matbench to entries within 150 meV of the hull and without noble gases. It is the second-largest task in the suite, large enough that graph networks clearly beat descriptor-based models. The task is also a reminder of a limit: PBE systematically underestimates measured gaps, so a model that predicts these labels perfectly still would not predict experiment, which is what the suite's `matbench_expt_gap` task is for.
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13	## Data and evaluation
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15	- Data: `matbench_mp_gap.json.gz` from MPContribs-ML (106,113 records, each a pymatgen Structure and a PBE gap in eV), at <https://ml.materialsproject.org/projects/matbench_mp_gap> or via `task.load()` in the `matbench` package; the gaps are Materials Project calculations.
16	- Split: five folds fixed in `matbench_v0.1_validation.json`, built with scikit-learn's `KFold(n_splits=5, shuffle=True, random_state=18012019)`. Train on each fold's training set, tune inside it only, predict its test set, report the mean. Changing the folds invalidates the comparison.
17	- Metric: mean absolute error in eV over the five test folds, lower is better. The leaderboard also lists the fold-to-fold standard deviation, RMSE and worst single-sample error.
18	- Official evaluation code: the `matbench` package (MIT), <https://github.com/materialsproject/matbench>; the task's `record(fold, predictions)`, as in `mb.matbench_mp_gap.record(...)` on a `MatbenchBenchmark` `mb`, scores a fold and `mb.to_file(...)` writes the submission file.
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20	## What is known
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22	- Top of the task leaderboard: coGN, mean MAE 0.1559 eV (standard deviation 0.0017).
23	- Runners-up: DeeperGATGNN 0.1694, coNGN 0.1697 and ALIGNN 0.1861 eV.
24	- A dummy regressor scores 1.3272 eV against a mean absolute deviation of 1.3271.
25	- The leaders' worst-case errors stay around 7 eV, so some compounds are still missed by several electronvolts.
26	- The leaderboard's repository has merged no submission since 20 January 2024; newer ones wait as open [pull requests](https://github.com/materialsproject/matbench/pulls), so the official numbers may lag what has been achieved.
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28	## What would count as progress
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30	- A mean MAE below 0.1559 eV on the five fixed folds, beating coGN, submitted as the `matbench` benchmark file so every fold's predictions can be re-scored.
31	- Code and a pinned environment that reproduce the run from the fixed folds.
32	- Per-fold numbers, not just the mean, so that a gain can be compared with the leader fold by fold on the same folds; a small gain should hold on each fold or over repeated training runs. Reproducing the current leader counts too, since the board has not moved since 2024.
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34	## References
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36	1. A. Jain et al., "The Materials Project: A materials genome approach to accelerating materials innovation", APL Materials 1, 011002 (2013). <https://doi.org/10.1063/1.4812323>
37	2. A. Dunn, Q. Wang, A. Ganose, D. Dopp and A. Jain, "Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm", npj Computational Materials 6, 138 (2020). <https://doi.org/10.1038/s41524-020-00406-3>
38	3. R. Ruff, P. Reiser, J. Stühmer and P. Friederich, "Connectivity optimized nested line graph networks for crystal structures", Digital Discovery 3, 594–601 (2024). <https://doi.org/10.1039/D4DD00018H>
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40	## Source
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42	Imported from [Matbench v0.1 matbench_mp_gap](https://matbench.materialsproject.org/Leaderboards%20Per-Task/matbench_v0.1_matbench_mp_gap/), curated by Materials Project (Dunn et al.). Open according to the source as of 2026-10-20. The statement above is written in our own words; see the source for the original wording, history and discussion.