Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917279573213184 |
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| author | Hamilton, Brenden W. Jones, Travis E. Germann, Timothy C. Nebgen, Benjamin T. |
| author_facet | Hamilton, Brenden W. Jones, Travis E. Germann, Timothy C. Nebgen, Benjamin T. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16628 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study Hamilton, Brenden W. Jones, Travis E. Germann, Timothy C. Nebgen, Benjamin T. Materials Science 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. |
| title | Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study |
| topic | Materials Science |
| url | https://arxiv.org/abs/2602.16628 |