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matbench_v0.1 matbench_mp_e_form

Individual Task Leaderboard for matbench_mp_e_form

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
CGCNN v2019 0.0337 0.0006 0.0682 7.7205
RF-SCM/Magpie 0.1165 0.0008 0.2419 5.4382
AMMExpress v2020 0.1726 0.0270 0.2602 5.8108
Dummy 1.0059 0.0030 1.1631 3.9096

Dataset info

Description

Matbench v0.1 test dataset for predicting DFT formation energy from structure. Adapted from Materials Project database. Removed entries having formation energy more than 3.0eV and those containing noble gases. Retrieved April 2, 2019. 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: 132752

Task type: regression

Input type: structure

Dataset columns
  • e_form: Target variable. Formation energy in eV as calculated by the Materials Project.
  • structure: Pymatgen Structure of the material.
Dataset reference

A. Jain*, S.P. Ong*, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson (*=equal contributions) The Materials Project: A materials genome approach to accelerating materials innovation APL Materials, 2013, 1(1), 011002. doi:10.1063/1.4812323

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',
                 '@article{Jain2013,\n'
                 'author = {Jain, Anubhav and Ong, Shyue Ping and Hautier, '
                 'Geoffroy and Chen, Wei and Richards, William Davidson and '
                 'Dacek, Stephen and Cholia, Shreyas and Gunter, Dan and '
                 'Skinner, David and Ceder, Gerbrand and Persson, Kristin '
                 'a.},\n'
                 'doi = {10.1063/1.4812323},\n'
                 'issn = {2166532X},\n'
                 'journal = {APL Materials},\n'
                 'number = {1},\n'
                 'pages = {011002},\n'
                 'title = {{The Materials Project: A materials genome approach '
                 'to accelerating materials innovation}},\n'
                 'url = '
                 '{http://link.aip.org/link/AMPADS/v1/i1/p011002/s1\\&Agg=doi},\n'
                 'volume = {1},\n'
                 'year = {2013}\n'
                 '}'],
 'columns': {'e_form': 'Target variable. Formation energy in eV as calculated '
                       'by the Materials Project.',
             'structure': 'Pymatgen Structure of the material.'},
 'description': 'Matbench v0.1 test dataset for predicting DFT formation '
                'energy from structure. Adapted from Materials Project '
                'database. Removed entries having formation energy more than '
                '3.0eV and those containing noble gases. Retrieved April 2, '
                '2019. 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': 'dedcb1d4ba2e3e50dbdd45ba5bc647a00e9c2bcf8f8bf556dc8e92caa39eb21f',
 'input_type': 'structure',
 'mad': 1.0059220443295362,
 'n_samples': 132752,
 'num_entries': 132752,
 'reference': 'A. Jain*, S.P. Ong*, G. Hautier, W. Chen, W.D. Richards, S. '
              'Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson '
              '(*=equal contributions)\n'
              'The Materials Project: A materials genome approach to '
              'accelerating materials innovation\n'
              'APL Materials, 2013, 1(1), 011002.\n'
              'doi:10.1063/1.4812323',
 'target': 'e_form',
 'task_type': 'regression',
 'unit': 'eV/atom',
 'url': 'https://ml.materialsproject.org/projects/matbench_mp_e_form.json.gz'}