Stoichiometry dependent properties of cerium hydride: An active learning developed interatomic potential study

Hamilton, Brenden William [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000245242201); Jones, Travis Elliott Loyd [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000189217641); Germann, Timothy Clark [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000000026813238X); Nebgen, Benjamin Tyler [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000153103263)

doi:10.1103/7mny-v7vt

DOE OSTI · 3382486

Stoichiometry dependent properties of cerium hydride: An active learning developed interatomic potential study

Hamilton, Brenden William [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000245242201)·Jones, Travis Elliott Loyd [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000189217641)·Germann, Timothy Clark [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000000026813238X)·Nebgen, Benjamin Tyler [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000153103263)

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Abstract

Cerium hydride has a variety of interesting properties, including a known lattice contraction and densification with increasing hydrogen content. However, precise stoichiometric control is not experimentally straightforward and ab initio approaches are not computationally feasible for many properties such as melting and low temperature diffusion. Therefore, we develop a machine-learned interatomic potential for cerium hydride that is valid for H to Ce ratios from 2.0 to 3.0. A query-by-committee active learning approach is used to develop the training set. Leveraging classical molecular dynamics simulations, we assess a range of properties and provide fundamental mechanisms for the trends with stoichiometry. Finally, a majority of the properties follow the trend of lattice contraction, being governed by the stronger lattice binding induced by adding octahedral atoms.

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36 MATERIALS SCIENCE crystal stoichiometry density functional theory machine learning machine learning models molecular dynamics

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Hamilton, Brenden William [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000245242201), Jones, Travis Elliott Loyd [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000189217641), Germann, Timothy Clark [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000000026813238X), Nebgen, Benjamin Tyler [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000153103263). 2026-07-20. Stoichiometry dependent properties of cerium hydride: An active learning developed interatomic potential study. https://doi.org/10.1103/7mny-v7vt

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