Predicting steel yield strength from composition

Predict the yield strength of a steel in MPa from its chemical formula alone, over 312 measured alloys.

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1	# Predicting steel yield strength from composition
2
3	Alloy designers would like to know the yield strength of a steel before making it. This task asks for that number from the chemical formula alone, with no processing history and no microstructure, on a small set of 312 measured alloys.
4
5	## Task
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7	Given a chemical composition as a formula string, predict the experimentally measured yield strength in megapascals. Scoring is the mean absolute error over the five fixed folds, so a result is one number per test alloy and nothing else. With 312 alloys in total, each test fold holds only 62 or 63 of them.
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9	## Background
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11	The data was retrieved from Citrine Informatics and deduplicated for Matbench v0.1, the 13-task suite Dunn and co-workers assembled in 2020 so that materials-ML methods are compared on the same data and splits. It is the smallest task in the suite and the only one about a mechanical property of an engineering alloy, which makes it the suite's test of whether a method can learn from a few hundred samples without overfitting. Yield strength depends strongly on processing, so composition cannot explain all of it; the interesting question is how much it can explain.
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13	## Data and evaluation
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15	- Data: `matbench_steels.json.gz` from MPContribs-ML (312 records, each a formula string and a yield strength in MPa), at <https://ml.materialsproject.org/projects/matbench_steels> or via `task.load()` in the `matbench` package; the set is derived from a Citrine Informatics dataset.
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 MPa 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_steels.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: TPOT-Mat, mean MAE 79.9468 MPa over the five folds (fold-to-fold standard deviation 13.5883).
23	- Runners-up: AutoML-Mat 82.3043 MPa and MODNet v0.1.12 87.7627 MPa. The first two were submitted for this task only; MODNet v0.1.12, which was run on all 13 tasks, is the best of the site's general-purpose entries.
24	- A mean-predicting dummy scores 229.7445 MPa, close to the target's mean absolute deviation of 229.3743 MPa, so the best model cuts the error by about two thirds.
25	- AutoML pipelines lead, ahead of the neural composition model CrabNet (107.3160 MPa) and the fine-tuned language models Darwin (123.2932 MPa) and gptchem (143.0028 MPa). Graph networks cannot enter, since the task gives no structures.
26	- Unmerged claim: pull request [#371](https://github.com/materialsproject/matbench/pull/371) (October 2026) reports 75.5426 MPa for Atomic-AffQRF, a composition-only ensemble of extra-trees models; it has not been reviewed or added to the leaderboard.
27	- 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.
28
29	## What would count as progress
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31	- A mean MAE below 79.9468 MPa on the five fixed folds, beating TPOT-Mat, submitted as the `matbench` benchmark file so every fold's predictions can be re-scored.
32	- Code and a pinned environment that reproduce the run from the fixed folds.
33	- 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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35	## References
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37	1. R. S. Olson, N. Bartley, R. J. Urbanowicz and J. H. Moore, "Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science", GECCO '16, 485–492 (2016). <https://doi.org/10.1145/2908812.2908918>
38	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>
39	3. 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>
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41	## Source
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43	Imported from [Matbench v0.1 matbench_steels](https://matbench.materialsproject.org/Leaderboards%20Per-Task/matbench_v0.1_matbench_steels/), 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.