Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability

Witman, Matthew; Ek, Gustav; Ling, Sanliang; Chames, Jeffery; Agarwal, Sapan; Wong, Justin; Allendorf, Mark D.; Sahlberg, Martin; Stavila, Vitalie

Abstract

Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet, the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial and error. Data-driven materials’ discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here, we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitate a data-driven approach that does not rely on exact crystal structures to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based on not only targeted thermodynamic properties but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70× increase in equilibrium pressure, 20 kJ/molH2 decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfHx. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient material selection for various applications, especially in areas such as metal hydride-based hydrogen compressors, actuators, and heat pumps.

Citation

Witman, M., Ek, G., Ling, S., Chames, J., Agarwal, S., Wong, J., …Stavila, V. (2021). Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability. Chemistry of Materials, 33(11), 4067–4076. https://doi.org/10.1021/acs.chemmater.1c00647

• Journal Article Type: Article
• Acceptance Date: Apr 19, 2021
• Online Publication Date: Apr 28, 2021
• Publication Date: Jun 8, 2021
• Deposit Date: May 1, 2021
• Publicly Available Date: Apr 29, 2022
• Journal: Chemistry of Materials
• Print ISSN: 0897-4756
• Electronic ISSN: 1520-5002
• Publisher: American Chemical Society
• Peer Reviewed: Peer Reviewed
• Volume: 33
• Issue: 11
• Pages: 4067–4076
• DOI: https://doi.org/10.1021/acs.chemmater.1c00647
• Keywords: Materials Chemistry; General Chemistry; General Chemical Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/5504870
• Publisher URL: https://pubs.acs.org/doi/10.1021/acs.chemmater.1c00647#
• Additional Information: This document is the Accepted Manuscript version of a Published Work that appeared in final form in Chemistry of Materials,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.chemmater.1c00647#