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

The ability to rapidly screen material performance in the vast space of compositionally complex (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 10,000s of candidate equimolar alloys. We critically show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy 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) and provide rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Selected target materials 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.