Thermodynamic Modeling of Complex Solid Solutions in the Lu-H-N System via Graph Neural Network Accelerated Monte Carlo Simulations

Journal Article · Tue Sep 02 00:00:00 EDT 2025 · PRX Energy

DOI:https://doi.org/10.1103/bsxd-qtph· OSTI ID:2588304

Guan, Pin-Wen ^[1]; Spataru, Catalin D. ^[1]; Stavila, Vitalie ^[1]; Jones, Reese Edward ^[1]; Sharma, Peter Anand ^[1]; Witman, Matthew David ^[1]

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1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Metal hydrides are important across diverse applications, such as hydrogen storage, batteries, gas sensors, nuclear reactions, and high-temperature superconductivity. Previous computational studies of metal hydrides under extreme pressures, e.g., 𝑂⁡(10²) ⁢GPa, usually treat them as stoichiometric compounds without considering interstitial lattice disorder. As pressures become more moderate in the 𝑂⁡(10⁰) ⁢GPa and below range, hydrogen disorder at interstitial lattice sites becomes prominent, e.g., in the N-doped Lu hydride that was recently claimed superconducting near 1 GPa. Further adding compositional complexity from alloying and/or multielement interstitial occupation makes elucidating pressure- and temperature-dependent observables intractable by first-principles calculations alone. We therefore propose a lattice graph neural-network surrogate modeling approach to predict configuration- and pressure-dependent equation-of-state properties. Their efficiency permits Monte Carlo simulations to calculate Gibbs energies and pressure-dependent phase diagrams, thereby revealing insights into the synthesis conditions required for achieving desired phase equilibria. We demonstrate this concept for the compositionally complex cubic Lu(H,N,Va)₃ system where three constituents (hydrogen, nitrogen and vacancy) have disordered multielement interstitial occupancies and insights into pressure-dependent phase equilibria are critically needed, e.g., N-doping levels can significantly lower dehydrogenation temperatures and provide a new strategy to optimize hydrogen-storage alloys. This work can improve the thermodynamic understanding of the Lu-H-N system and help rational synthesis of N-doped Lu hydrides, but more generally demonstrates an efficient approach to model pressure-dependent thermodynamics of multicomponent solid solutions.

• Research Organization: Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0003525
• OSTI ID: 2588304
• Report Number(s): SAND--2025-11245J; 1767954
• Journal Information: PRX Energy, Journal Name: PRX Energy Journal Issue: 3 Vol. 4; ISSN 2768-5608
• Publisher: American Physical Society (APS)
• Country of Publication: United States
• Language: English

Related Subjects

Monte Carlo methods
artificial neural networks
hydrides
hydrogen storage
phase diagrams
pressure effects
solid solutions
thermodynamics