Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning
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. 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.
- Authors:
-
[1];
[2];
[1];
[1]
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Univ. of Nottingham, University Park, Nottingham (United Kingdom)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Hydrogen Fuel Cell Technologies Office
- OSTI Identifier:
- 1595021
- Report Number(s):
- SAND-2019-14626J
Journal ID: ISSN 1948-7185; 682475; TRN: US2100658
- Grant/Contract Number:
- AC04-94AL85000; NA-0003525
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Physical Chemistry Letters
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 1948-7185
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; machine learning; metal hydrides
Citation Formats
Witman, Matthew, Ling, Sanliang, Grant, David M., Walker, Gavin S., Agarwal, Sapan, Stavila, Vitalie, and Allendorf, Mark D. Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning. United States: N. p., 2019.
Web. doi:10.1021/acs.jpclett.9b02971.
Witman, Matthew, Ling, Sanliang, Grant, David M., Walker, Gavin S., Agarwal, Sapan, Stavila, Vitalie, & Allendorf, Mark D. Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning. United States. https://doi.org/10.1021/acs.jpclett.9b02971
Witman, Matthew, Ling, Sanliang, Grant, David M., Walker, Gavin S., Agarwal, Sapan, Stavila, Vitalie, and Allendorf, Mark D. Sat .
"Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning". United States. https://doi.org/10.1021/acs.jpclett.9b02971. https://www.osti.gov/servlets/purl/1595021.
@article{osti_1595021,
title = {Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning},
author = {Witman, Matthew and Ling, Sanliang and Grant, David M. and Walker, Gavin S. and Agarwal, Sapan and Stavila, Vitalie and Allendorf, Mark D.},
abstractNote = {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.},
doi = {10.1021/acs.jpclett.9b02971},
journal = {Journal of Physical Chemistry Letters},
number = 1,
volume = 11,
place = {United States},
year = {2019},
month = {12}
}
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