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matbench_v0.1 matbench_steels

Individual Task Leaderboard for matbench_steels

Leaderboard for an individual task. Algorithms shown here may include both general purpose and specialized algorithms (i.e., algorithms which are only valid for a subset of tasks in the benchmark.

Leaderboard

algorithm mean mae std mae mean rmse max max_error
AMMExpress v2020 97.4929 13.7919 154.0161 1142.9223
RF-SCM/Magpie 103.5125 11.0368 149.3839 1121.1276
Dummy 229.7445 9.6958 301.2211 1088.0568

Dataset info

Description

Matbench v0.1 test dataset for predicting steel yield strengths from chemical composition alone. Retrieved from Citrine informatics. Deduplicated. For benchmarking w/ nested cross validation, the order of the dataset must be identical to the retrieved data; refer to the Automatminer/Matbench publication for more details.

Number of samples: 312

Task type: regression

Input type: composition

Dataset columns
  • composition: Chemical formula.
  • yield strength: Target variable. Experimentally measured steel yield strengths, in MPa.
Dataset reference

https://citrination.com/datasets/153092/

Metadata

{'bibtex_refs': ['@Article{Dunn2020,\n'
                 'author={Dunn, Alexander\n'
                 'and Wang, Qi\n'
                 'and Ganose, Alex\n'
                 'and Dopp, Daniel\n'
                 'and Jain, Anubhav},\n'
                 'title={Benchmarking materials property prediction methods: '
                 'the Matbench test set and Automatminer reference '
                 'algorithm},\n'
                 'journal={npj Computational Materials},\n'
                 'year={2020},\n'
                 'month={Sep},\n'
                 'day={15},\n'
                 'volume={6},\n'
                 'number={1},\n'
                 'pages={138},\n'
                 'abstract={We present a benchmark test suite and an automated '
                 'machine learning procedure for evaluating supervised machine '
                 'learning (ML) models for predicting properties of inorganic '
                 'bulk materials. The test suite, Matbench, is a set of '
                 '13{\\thinspace}ML tasks that range in size from 312 to 132k '
                 'samples and contain data from 10 density functional '
                 'theory-derived and experimental sources. Tasks include '
                 'predicting optical, thermal, electronic, thermodynamic, '
                 "tensile, and elastic properties given a material's "
                 'composition and/or crystal structure. The reference '
                 'algorithm, Automatminer, is a highly-extensible, fully '
                 'automated ML pipeline for predicting materials properties '
                 'from materials primitives (such as composition and crystal '
                 'structure) without user intervention or hyperparameter '
                 'tuning. We test Automatminer on the Matbench test suite and '
                 'compare its predictive power with state-of-the-art crystal '
                 'graph neural networks and a traditional descriptor-based '
                 'Random Forest model. We find Automatminer achieves the best '
                 'performance on 8 of 13 tasks in the benchmark. We also show '
                 'our test suite is capable of exposing predictive advantages '
                 'of each algorithm---namely, that crystal graph methods '
                 'appear to outperform traditional machine learning methods '
                 'given {\\textasciitilde}104 or greater data points. We '
                 'encourage evaluating materials ML algorithms on the Matbench '
                 'benchmark and comparing them against the latest version of '
                 'Automatminer.},\n'
                 'issn={2057-3960},\n'
                 'doi={10.1038/s41524-020-00406-3},\n'
                 'url={https://doi.org/10.1038/s41524-020-00406-3}\n'
                 '}\n',
                 '@misc{Citrine Informatics,\n'
                 'title = {Mechanical properties of some steels},\n'
                 'howpublished = '
                 '{\\url{https://citrination.com/datasets/153092/},\n'
                 '}'],
 'columns': {'composition': 'Chemical formula.',
             'yield strength': 'Target variable. Experimentally measured steel '
                               'yield strengths, in MPa.'},
 'description': 'Matbench v0.1 test dataset for predicting steel yield '
                'strengths from chemical composition alone. Retrieved from '
                'Citrine informatics. Deduplicated. For benchmarking w/ nested '
                'cross validation, the order of the dataset must be identical '
                'to the retrieved data; refer to the Automatminer/Matbench '
                'publication for more details.',
 'file_type': 'json.gz',
 'hash': '473bc4957b2ea5e6465aef84bc29bb48ac34db27d69ea4ec5f508745c6fae252',
 'input_type': 'composition',
 'mad': 229.37426857330706,
 'n_samples': 312,
 'num_entries': 312,
 'reference': 'https://citrination.com/datasets/153092/',
 'target': 'yield strength',
 'task_type': 'regression',
 'unit': 'MPa',
 'url': 'https://ml.materialsproject.org/projects/matbench_steels.json.gz'}