Predicting experimental band gaps from a chemical formula

Band gaps decide whether a compound is a metal, a semiconductor or an insulator, and measured gaps are scarcer than computed ones. This task asks for the experimental gap from the chemical formula alone, on 4,604 inorganic solids collected from the literature.

Task

Given a chemical composition as a formula string, predict the experimentally measured band gap in electronvolts. Scoring is the mean absolute error over the five fixed folds. Structure is deliberately withheld, so the task measures how far composition alone can go.

Background

The set was taken from the supplementary data of Zhuo, Mansouri Tehrani and Brgoch, who compiled reported gaps and fitted a machine-learning model to them; Matbench deduplicated it by composition, dropping compositions whose reported gaps spread over more than 0.1 eV. Because many entries are zero-gap metals, the target distribution is spiky, and the companion classification task (matbench_expt_is_metal) asks only for the metal/non-metal call. It is the largest composition-only regression task in the suite.

Data and evaluation

• Data: matbench_expt_gap.json.gz from MPContribs-ML (4,604 records, each a formula string and a measured gap in eV), at https://ml.materialsproject.org/projects/matbench_expt_gap or via task.load() in the matbench package; the gaps were compiled by Zhuo and co-workers.
• 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.
• 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.
• Official evaluation code: the matbench package (MIT), https://github.com/materialsproject/matbench; the task's record(fold, predictions), as in mb.matbench_expt_gap.record(...) on a MatbenchBenchmark mb, scores a fold and mb.to_file(...) writes the submission file.

What is known

• Top of the task leaderboard: Darwin, mean MAE 0.2865 eV (standard deviation 0.0083), a fine-tuned large language model submitted for the composition tasks only.
• Next on the board: Ax/SAASBO CrabNet v1.2.7 at 0.3310 eV and MODNet v0.1.12 at 0.3327 eV. MODNet v0.1.12, which was run on all 13 tasks, is the best of the site's general-purpose entries.
• A dummy regressor scores 1.1435 eV (mean absolute deviation 1.1432), so the best models cut the error about fourfold.
• Unmerged claims: pull request #364 (August 2026) reports 0.2775 eV for a gradient-boosting blend gated by a metal classifier, and #372 (October 2026) reports 0.2352 eV for a model stack that also takes Materials Project DFT gaps and formation energies as input features. Neither has been reviewed or added to the leaderboard.
• The leaderboard's repository has merged no submission since 20 January 2024; newer ones wait as open pull requests, so the official numbers may lag what has been achieved.

What would count as progress

• A mean MAE below 0.2865 eV on the five fixed folds, beating Darwin, submitted as the matbench benchmark file so every fold's predictions can be re-scored.
• Code and a pinned environment that reproduce the run from the fixed folds.
• 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.

References

1. Y. Zhuo, A. Mansouri Tehrani and J. Brgoch, "Predicting the Band Gaps of Inorganic Solids by Machine Learning", The Journal of Physical Chemistry Letters 9, 1668–1673 (2018). https://doi.org/10.1021/acs.jpclett.8b00124
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
3. T. Xie et al., "Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT" (2023). https://arxiv.org/abs/2304.02213
4. P.-P. De Breuck, M. L. Evans and G.-M. Rignanese, "Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet", Journal of Physics: Condensed Matter 33, 404002 (2021). https://doi.org/10.1088/1361-648X/ac1280

Source

Imported from Matbench v0.1 matbench_expt_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.