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Learning properties of ordered and disordered materials from multi-fidelity data

Nature Computational Science volume 1, pages 46–53 (2021)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a new paradigm to provide rapid predictions of materials properties, their practical utility is limited by the scarcity of high-fidelity data. Here, we develop multi-fidelity graph networks as a universal approach to achieve accurate predictions of materials properties with small data sizes. As a proof of concept, we show that the inclusion of low-fidelity Perdew–Burke–Ernzerhof band gaps greatly enhances the resolution of latent structural features in materials graphs, leading to a 22–45% decrease in the mean absolute errors of experimental band gap predictions. We further demonstrate that learned elemental embeddings in materials graph networks provide a natural approach to model disorder in materials, addressing a fundamental gap in the computational prediction of materials properties.

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Fig. 1: Multi-fidelity materials graph networks.
Fig. 2: Test MAEs of multi-fidelity graph network model predictions on ordered crystal band gaps.
Fig. 3: Effect of including low-fidelity PBE data on latent structural features.
Fig. 4: Performance of disordered multi-fidelity graph network models.

Data availability

Multi-fidelity band gap data and molecular data are available at https://doi.org/10.6084/m9.figshare.1304033048. The data for all figures and extended data figures are available in Source Data.

Code availability

Model fitting and results plotting codes are available at https://github.com/materialsvirtuallab/megnet/tree/master/multi-fidelity. MEGNet is available at https://github.com/materialsvirtuallab/megnet. The specific version of the package can be found at https://doi.org/10.5281/zenodo.407202950.

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Acknowledgements

This work was primarily supported by the Materials Project, funded by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract no. DE-AC02-05-CH11231: Materials Project program KC23MP. The authors also acknowledge support from the National Science Foundation SI2-SSI Program under award no. 1550423 for the software development portions of the work. C.C. thanks M. Horton for his assistance with the GLLB-SC data set.

Author information

Authors and Affiliations

  1. Department of NanoEngineering, University of California, San Diego, CA, USA

    Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li & Shyue Ping Ong

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Contributions

C.C. and S.P.O. conceived the idea and designed the work. C.C. implemented the models and performed the analysis. S.P.O. supervised the project. Y.Z., W.Y. and X.L. helped with the data collection and analysis. C.C. and S.P.O. wrote the manuscript. All authors contributed to discussions and revisions.

Corresponding author

Correspondence to Shyue Ping Ong.

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The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Keith Tobias Butler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Fernando Chirigati was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Five-fidelity model test error distributions.

a, The model errors decomposed into metals vs non-metals and (b) the test error distributions. The ‘metal-clip’ category means that the predicted negative band gaps are clipped at zero.

Source data

Extended Data Fig. 2 Band gap data distribution and correlation.

Plots of the pairwise relationship between band gaps from different fidelity sources. The band gap distribution in each data set is presented along the top diagonal, and the Pearson correlation coefficient r between each pair of data are annotated in each plot.

Source data

Extended Data Fig. 3 Predicted experimental band gaps of Ba0.5Sr0.5CoxFe1−xO3−δ using 4-fi models.

Both the Co ratio x and oxygen non-stoichiometry δ are changed to chart the two dimension band gap space.

Source data

Extended Data Fig. 4 Multi-fidelity modeling of energies of molecules.

a, Average MAE in G4MP2 energy predictions for the QM9 data set using 1-fi G4MP2 models and 2-fi B3LYP/G4MP2 models trained with different G4MP2 data sizes. b, Average MAE in CCSD(T) energy predictions for the QM7b data set using 1-fi CCSD(T) models, 2-fi HF/CCSD(T) and MP2/CCSD(T) models, and 3-fi HF/MP2/CCSD(T) models. s is the ratio of data sizes. s = 1 and 2 correspond to CCSD(T):MP2:HF ratios of 1:2:4 and 1:4:16, respectively. The error bars indicate one standard deviation.

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1, Tables 1–6 and discussion.

Supplementary Data 1

Data statistics and average MAEs with standard deviations of multi-fidelity graph network models trained on different combinations of fidelities. The data size Nd and the MAD are listed for each fidelity. For the model error section, the leftmost columns indicate the combination of fidelities used to train the model and the other columns are the model MAEs in eV on the corresponding test data fidelity. The errors are reported by the mean and standard deviation of the MAEs using six random data splits.

Supplementary Data 2

Model test MAE comparisons for transfer learning, 2-fi models and 5-fi models. The first column is the model category and the other columns are the average model MAEs with standard deviation on the corresponding test data fidelity.

Supplementary Data 3

Average MAEs of 2-fi and 4-fi graph network models trained using non-overlapping-structure data split. The first column shows the data fidelity combinations in training the models, and the other columns are the average MAEs with standard deviations on the corresponding test data fidelity.

Source data

Source Data Fig. 2

Statistical Source Data

Source Data Fig. 3

Statistical Source Data

Source Data Fig. 4

Statistical Source Data

Source Data Extended Data Fig. 1

Statistical Source Data

Source Data Extended Data Fig. 2

Statistical Source Data

Source Data Extended Data Fig. 3

Statistical Source Data

Source Data Extended Data Fig. 4

Statistical Source Data

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Chen, C., Zuo, Y., Ye, W. et al. Learning properties of ordered and disordered materials from multi-fidelity data. Nat Comput Sci 1, 46–53 (2021). https://doi.org/10.1038/s43588-020-00002-x

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