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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 | +{ | |
| 2 | + "domain": "chemistry-materials", | |
| 3 | + "topics": [ | |
| 4 | + "materials-science" | |
| 5 | + ], | |
| 6 | + "type": "algorithm", | |
| 7 | + "benchmark": { | |
| 8 | + "task": "Predict steel yield strength from composition", | |
| 9 | + "metric": { | |
| 10 | + "name": "MAE (MPa)", | |
| 11 | + "better": "lower" | |
| 12 | + }, | |
| 13 | + "baseline": { | |
| 14 | + "name": "Best on the Matbench leaderboard (TPOT-Mat)", | |
| 15 | + "score": 79.9468 | |
| 16 | + } | |
| 17 | + }, | |
| 18 | + "source": { | |
| 19 | + "key": "matbench", | |
| 20 | + "name": "Matbench", | |
| 21 | + "url": "https://matbench.materialsproject.org/Leaderboards%20Per-Task/matbench_v0.1_matbench_steels/", | |
| 22 | + "id": "matbench_steels", | |
| 23 | + "curator": "Materials Project (Dunn et al.)", | |
| 24 | + "poser": "Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp and Anubhav Jain", | |
| 25 | + "status": "open; leaderboard last updated January 2024", | |
| 26 | + "as_of": "2026-10-20", | |
| 27 | + "catalog": "0521c19bbd38" | |
| 28 | + } | |
| 29 | +} | |