Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study

TL;DR

This study develops a machine-learned interatomic potential for cerium hydride to accurately predict properties across various stoichiometries, enabling insights into structural trends and fundamental mechanisms without extensive computational costs.

Contribution

The paper introduces an active learning-based interatomic potential for cerium hydride covering H to Ce ratios from 2.0 to 3.0, facilitating property predictions and mechanistic understanding.

Findings

Properties follow lattice contraction trend with increased hydrogen content

Stronger lattice binding is induced by octahedral atom addition

The potential enables efficient property assessment across stoichiometries

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 {\it 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. A majority of the properties follow the trend of lattice contraction, being governed by the stronger lattice binding induced by adding octahedral atoms.