Towards Pareto optimal high entropy hydrides via data-driven materials discovery

Witman, Matthew D.; Ling, Sanliang; Wadge, Matthew; Bouzidi, Anis; Pineda-Romero, Nayely; Clulow, Rebecca; Ek, Gustav; Chames, Jeffery M.; Allendorf, Emily J.; Agarwal, Sapan; Allendorf, Mark D.; Walker, Gavin S.; Grant, David M.; Sahlberg, Martin; Zlotea, Claudia; Stavila, Vitalie

Abstract

The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ∼5 kJ molH2−1) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ molH2−1) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.

Citation

Witman, M. D., Ling, S., Wadge, M., Bouzidi, A., Pineda-Romero, N., Clulow, R., Ek, G., Chames, J. M., Allendorf, E. J., Agarwal, S., Allendorf, M. D., Walker, G. S., Grant, D. M., Sahlberg, M., Zlotea, C., & Stavila, V. (2023). Towards Pareto optimal high entropy hydrides via data-driven materials discovery. Journal of Materials Chemistry A, 11(29), 15878-15888. https://doi.org/10.1039/d3ta02323k

• Journal Article Type: Article
• Acceptance Date: Jun 26, 2023
• Online Publication Date: Jul 7, 2023
• Publication Date: 2023-08
• Deposit Date: Jul 18, 2023
• Publicly Available Date: Jul 8, 2024
• Journal: Journal of Materials Chemistry A
• Print ISSN: 2050-7488
• Electronic ISSN: 2050-7496
• Publisher: Royal Society of Chemistry
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 29
• Pages: 15878-15888
• DOI: https://doi.org/10.1039/d3ta02323k
• Keywords: General Materials Science, Renewable Energy, Sustainability and the Environment, General Chemistry
• Public URL: https://nottingham-repository.worktribe.com/output/23005691
• Publisher URL: https://pubs.rsc.org/en/content/articlelanding/2023/ta/d3ta02323k