Towards Pareto optimal high entropy hydrides data-driven materials discovery

• Published in: Journal of materials chemistry. A, Materials for energy and sustainability Materials for energy and sustainability, 2023-07, Vol.11 (29), p.15878-15888
• Main Authors: 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
• Format: Article
• ISSN: 2050-7488; 2050-7496
• DOI: 10.1039/d3ta02323k

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 mol H 2 −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 mol H 2 −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. Data-driven predictions of metal hydride thermodynamic properties elucidate the Pareto optimal front of high entropy alloy candidates for hydrogen storage.