Machine-Learning Accelerated First-Principles Accurate Modeling of the Solid-Liquid Phase Transition in MgO under Mantle Conditions.
- DOI
- 10.1021/acs.jpclett.3c02424
- Published
- 2023 Oct 5
- Container
- The journal of physical chemistry letters
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jpclett.3c02424,
title = {Machine-Learning Accelerated First-Principles Accurate Modeling of the Solid-Liquid Phase Transition in MgO under Mantle Conditions.},
author = {Wisesa P and Andolina CM and Saidi WA},
year = {2023},
journal = {The journal of physical chemistry letters},
doi = {10.1021/acs.jpclett.3c02424},
url = {https://doi.org/10.1021/acs.jpclett.3c02424}
}RIS
TY - JOUR TI - Machine-Learning Accelerated First-Principles Accurate Modeling of the Solid-Liquid Phase Transition in MgO under Mantle Conditions. AU - Wisesa P AU - Andolina CM AU - Saidi WA PY - 2023 JO - The journal of physical chemistry letters DO - 10.1021/acs.jpclett.3c02424 UR - https://doi.org/10.1021/acs.jpclett.3c02424 ER -
APA
P, W., CM, A., & WA, S. (2023). Machine-Learning Accelerated First-Principles Accurate Modeling of the Solid-Liquid Phase Transition in MgO under Mantle Conditions.. The journal of physical chemistry letters. https://doi.org/10.1021/acs.jpclett.3c02424
Source records
- pubmed · retrieved 2026-09-25T02:43:13.380Z