e-space
Manchester Metropolitan University's Research Repository

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

    Witman, Matthew D ORCID logoORCID: https://orcid.org/0000-0001-6263-5114, Ling, Sanliang ORCID logoORCID: https://orcid.org/0000-0003-1574-7476, Wadge, Matthew ORCID logoORCID: https://orcid.org/0000-0002-5157-507X, Bouzidi, Anis, Pineda-Romero, Nayely, Clulow, Rebecca ORCID logoORCID: https://orcid.org/0000-0002-0096-4366, Ek, Gustav ORCID logoORCID: https://orcid.org/0000-0003-4831-3842, Chames, Jeffery M ORCID logoORCID: https://orcid.org/0000-0003-3799-3248, Allendorf, Emily J, Agarwal, Sapan, Allendorf, Mark D ORCID logoORCID: https://orcid.org/0000-0001-5645-8246, Walker, Gavin S, Grant, David M, Sahlberg, Martin, Zlotea, Claudia and Stavila, Vitalie ORCID logoORCID: https://orcid.org/0000-0003-0981-0432 (2023) Towards Pareto optimal high entropy hydrides via data-driven materials discovery. Journal of Materials Chemistry A, 11 (29). pp. 15878-15888. ISSN 2050-7488

    [img]
    Preview
    Accepted Version
    Available under License In Copyright.

    Download (4MB) | Preview

    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.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    79Downloads
    6 month trend
    18Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record