Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Energy materials screening with defect graph neural networks

Graph neural networks (GNNs) present a promising route for machine learning of solid-state materials’ properties, but methods capable of directly predicting defect properties from ideal, defect-free structures are needed. A GNN developed for direct defect property predictions enables high-throughput screening of redox-active oxides for energy applications and beyond.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Computational workflow and statistical analysis of the ML model.

References

  1. McDaniel, A. H. Renewable energy carriers derived from concentrating solar power and nonstoichiometric oxides. Curr. Opin. Green Sustain. 4, 37–43 (2017). A review article offering a perspective on the state of solar thermochemical gas splitting.

    Article  Google Scholar 

  2. Goyal, A., Gorai, P., Peng, H., Lany, S. & Stevanovic, V. A computational framework for automation of point defect calculations. Comput. Mater. Sci. 130, 1–9 (2017). This paper describes a high-throughput approach to supercell defect calculations.

    Article  Google Scholar 

  3. Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018). This work introduces the Crystal Graph Convolutional Neutral Networks as a machine learning approach for crystalline materials.

    Article  Google Scholar 

  4. Lany, S. Communication: the electronic entropy of charged defect formation and its impact on thermochemical redox cycles. J. Chem. Phys. 148, 071101 (2018). This article presents an analysis of the effect of the solid-state reduction entropy on thermochemical gas-phase equilibria.

    Article  Google Scholar 

  5. Wexler, R. B., Gautam, G. S., Stechel, E. B. & Carter, E. A. Factors governing oxygen vacancy formation in oxide perovskites. J. Am. Chem. Soc. 143, 13212–13227 (2021). This paper describes linear models to predict oxygen defect formation enthalpies.

    Article  Google Scholar 

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Witman, M. D. et al. Defect graph neural networks for materials discovery in high-temperature clean-energy applications. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00495-2 (2023).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Energy materials screening with defect graph neural networks. Nat Comput Sci 3, 671–672 (2023). https://doi.org/10.1038/s43588-023-00510-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-023-00510-6

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing