System and Machine Learning-Guided Materials Design for High-Pressure Hydrogen Compression

Witman, Matthew David; Davis, Brendan Calvin; Stavila, Vitalie; Johnson, Terry A.

doi:10.1021/acsaem.5c00887

System and Machine Learning-Guided Materials Design for High-Pressure Hydrogen Compression

Journal Article · Wed Jul 09 00:00:00 EDT 2025 · ACS Applied Energy Materials

DOI:https://doi.org/10.1021/acsaem.5c00887· OSTI ID:2585638

^[1]; Davis, Brendan Calvin ^[1]; ^[1]; Johnson, Terry A. ^[1]

Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

Cost-effective and reliable hydrogen compression remains a challenging barrier in the widespread adoption of hydrogen as an energy carrier. The prevailing technology of mechanical compression suffers from several drawbacks, some of which can be addressed by nonmechanical compression strategies (e.g., electrochemical or metal hydride-based thermal compression). Thermally driven metal hydride compression strategies typically rely on multistage metal hydride-based compressors; however, discovering or optimizing low-stability metal hydrides that can pressurize hydrogen upward of 1000 bar is difficult, both with respect to computational predictions and experimental validation. Here, in this study, we (1) demonstrate that simple machine learning-derived design rules can inform the rational design of alloying strategies yielding low-stability hydrides, (2) validate their experimental pressure–composition–temperature (PCT) isotherms up to 875 bar, and (3) utilize a dynamic system-level model of a metal hydride compressor design to evaluate their performance under realistic operating conditions. Importantly, this analysis yields predicted operational efficiencies of both 2-stage (90–875 bar) and 3-stage (20–875 bar) metal hydride compressors to enable further evaluation of this technology and its techno-economic outlook.

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: NA0003525

OSTI ID:: 2585638

Report Number(s):: SAND--2025-09455J; 1767969

Journal Information:: ACS Applied Energy Materials, Journal Name: ACS Applied Energy Materials Journal Issue: 14 Vol. 8; ISSN 2574-0962

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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