Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning

Witman, Matthew; Ling, Sanliang; Grant, David M.; Walker, Gavin S.; Agarwal, Sapan; Stavila, Vitalie; Allendorf, Mark D.

Abstract

An open question in the metal hydride community is whether there are simple, physics-based design rules that dictate the thermodynamic properties of these materials across the variety of structures and chemistry they can exhibit. While black box machine learning-based algorithms can predict these properties with some success, they do not directly provide the basis on which these predictions are made, therefore complicating the a priori design of novel materials exhibiting a desired property value. In this work we demonstrate how feature importance, as identified by a gradient boosting tree regressor, uncovers the strong dependence of the metal hydride equilibrium H2 pressure on a volume-based descriptor that can be computed from just the elemental composition of the intermetallic alloy. Elucidation of this simple structure–property relationship is valid across a range of compositions, metal substitutions, and structural classes exhibited by intermetallic hydrides. This permits rational targeting of novel intermetallics for high-pressure hydrogen storage (low-stability hydrides) by their descriptor values, and we predict a known intermetallic to form a low-stability hydride (as confirmed by density functional theory calculations) that has not yet been experimentally investigated.

Citation

Witman, M., Ling, S., Grant, D. M., Walker, G. S., Agarwal, S., Stavila, V., & Allendorf, M. D. (2020). Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning. Journal of Physical Chemistry Letters, 11(1), 40-47. https://doi.org/10.1021/acs.jpclett.9b02971

• Journal Article Type: Article
• Acceptance Date: Dec 7, 2019
• Online Publication Date: Dec 7, 2019
• Publication Date: Jan 2, 2020
• Deposit Date: Dec 21, 2019
• Publicly Available Date: Dec 8, 2020
• Journal: The Journal of Physical Chemistry Letters
• Electronic ISSN: 1948-7185
• Publisher: American Chemical Society
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 1
• Pages: 40-47
• DOI: https://doi.org/10.1021/acs.jpclett.9b02971
• Public URL: https://nottingham-repository.worktribe.com/output/3612090
• Publisher URL: https://pubs.acs.org/doi/10.1021/acs.jpclett.9b02971
• Additional Information: This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Physical Chemistry Letters copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://pubs.acs.org/doi/10.1021/acs.jpclett.9b02971