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

Brenden W. Hamilton, Travis E. Jones, Timothy C. Germann, Benjamin T. Nebgen

TL;DR

This work addresses how cerium hydride properties depend on hydrogen stoichiometry and the computational cost of ab initio methods. It develops a HIPNN-based machine-learned interatomic potential for CeH_X across 2.0 ≤ H/Ce ≤ 3.0 using a query-by-committee active-learning workflow, with DFT labels obtained from PBE+U calculations. The final potential enables large-scale MD studies, revealing that lattice contraction and elastic stiffening generally track increasing octahedral hydrogen content, while melting and diffusion show more nuanced, non-monotonic responses across stoichiometries. Overall, the approach provides stoichiometry-resolved insights at scales beyond direct ab initio calculations, informing design strategies for cerium hydride materials.

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.

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

TL;DR

This work addresses how cerium hydride properties depend on hydrogen stoichiometry and the computational cost of ab initio methods. It develops a HIPNN-based machine-learned interatomic potential for CeH_X across 2.0 ≤ H/Ce ≤ 3.0 using a query-by-committee active-learning workflow, with DFT labels obtained from PBE+U calculations. The final potential enables large-scale MD studies, revealing that lattice contraction and elastic stiffening generally track increasing octahedral hydrogen content, while melting and diffusion show more nuanced, non-monotonic responses across stoichiometries. Overall, the approach provides stoichiometry-resolved insights at scales beyond direct ab initio calculations, informing design strategies for cerium hydride materials.

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.
Paper Structure (6 sections, 6 figures)

This paper contains 6 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Methods and Interatomic Potential
  3. Mechanical Properties
  4. Melting and Hydrogen Diffusion
  5. Conclusions
  6. Acknowledgments

Figures (6)

  • Figure 1: Force and energy parity histograms for the HIPNN CeH model.
  • Figure 2: Equilibrium, 300 K density and lattice parameters for a range of CeH$_X$ stoichiometries.
  • Figure 3: Equilibrium, 300 K elastic constants for a range of CeH stoichiometries. $C_{11}$ corresponds to the left $y$ axis, $C_{12}$ and $C_{44}$ to the right axis.
  • Figure 4: Heat map of the pressure response of CeH$_X$ for varying stoichiometry and hydrostatic compressions. Points in the upper right corner undergo compression-induced yielding.
  • Figure 5: Stoichiometric dependent melting points for CeH$_X$ at 1 bar of pressure.
  • ...and 1 more figures