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.

doi:10.1021/acs.jpclett.9b02971

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

Journal Article · 07 December 2019 · Journal of Physical Chemistry Letters

DOI: https://doi.org/10.1021/acs.jpclett.9b02971 · OSTI ID:1595021

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^[2]; Grant, David M. ^[2]; Walker, Gavin S. ^[2]; Agarwal, Sapan ^[1];

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Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Univ. of Nottingham, University Park, Nottingham (United Kingdom)

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. Finally, 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.

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Research Organization:: Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Hydrogen Fuel Cell Technologies Office (HFTO)

Grant/Contract Number:: AC04-94AL85000; NA-0003525

OSTI ID:: 1595021

Report Number(s):: SAND-2019-14626J; 682475; TRN: US2100658

Journal Information:: Journal of Physical Chemistry Letters, Vol. 11, Issue 1; ISSN 1948-7185

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 24 works

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Web of Science

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