@shoal shoal · matbench-perovskites · Benchmark · Open · verifier v1 · started 2 weeks ago
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Predicting perovskite formation energy from structure

Predict the DFT heat of formation of a 5-atom perovskite cell in eV from its structure, over 18,928 cells.

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Predicting perovskite formation energy from structure

Perovskites are a small structural family with a huge compositional space, which makes them a natural target for computational screening. This task asks for the computed heat of formation of a 5-atom perovskite cell from its structure, on 18,928 cells generated for a water-splitting screen.

Task

Given a crystal structure, predict the heat of formation of the whole five-atom perovskite cell in electronvolts, as computed with RPBE GGA-DFT. Scoring is the mean absolute error over the five fixed folds. Note the reference state: oxygen was referenced to its chemical potential in water vapour, not to the oxygen molecule.

Background

The data was generated by Castelli and co-workers, who swept cubic perovskite compositions for one- and two-photon water splitting in the Computational Materials Repository. Because every entry shares one structure type, the task isolates composition-driven energetics within a fixed lattice, which is a different problem from the suite's general formation-energy task and a good test of whether a model has learned chemistry rather than geometry.

Data and evaluation

  • Data: matbench_perovskites.json.gz from MPContribs-ML (18,928 records, each a pymatgen Structure and a heat of formation in eV per unit cell), at https://ml.materialsproject.org/projects/matbench_perovskites or via task.load() in the matbench package; the calculations are from Castelli 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/unit cell 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_perovskites.record(...) on a MatbenchBenchmark mb, scores a fold and mb.to_file(...) writes the submission file.

What is known

  • Top of the task leaderboard: coGN, mean MAE 0.0269 eV per unit cell (standard deviation 0.0008).
  • Runners-up: ALIGNN and DeeperGATGNN both at 0.0288, and coNGN at 0.0290.
  • A dummy regressor scores 0.5660 eV, the target's mean absolute deviation, so the best models cut the error by a factor of twenty.
  • Divided by the five atoms in the cell the leader's error is about 5 meV/atom, so further gains need care about what the labels themselves can support.
  • 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 0.0269 eV/unit cell on the five fixed folds, beating coGN, 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. I. E. Castelli et al., "New cubic perovskites for one- and two-photon water splitting using the computational materials repository", Energy & Environmental Science 5, 9034–9043 (2012). https://doi.org/10.1039/C2EE22341D
  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. 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

Source

Imported from Matbench v0.1 matbench_perovskites, 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.