Physics-Inspired Evolutionary Machine Learning Method: From the Schrödinger Equation to an Orbital-Free-DFT Kinetic Energy Functional.
- DOI
- 10.1021/acs.jpca.4c04155
- Published
- 2024 Oct 10
- Container
- The journal of physical chemistry. A
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jpca.4c04155,
title = {Physics-Inspired Evolutionary Machine Learning Method: From the Schrödinger Equation to an Orbital-Free-DFT Kinetic Energy Functional.},
author = {Rodríguez JI and Vergara-Beltrán UA},
year = {2024},
journal = {The journal of physical chemistry. A},
doi = {10.1021/acs.jpca.4c04155},
url = {https://doi.org/10.1021/acs.jpca.4c04155}
}RIS
TY - JOUR TI - Physics-Inspired Evolutionary Machine Learning Method: From the Schrödinger Equation to an Orbital-Free-DFT Kinetic Energy Functional. AU - Rodríguez JI AU - Vergara-Beltrán UA PY - 2024 JO - The journal of physical chemistry. A DO - 10.1021/acs.jpca.4c04155 UR - https://doi.org/10.1021/acs.jpca.4c04155 ER -
APA
JI, R., & UA, V. (2024). Physics-Inspired Evolutionary Machine Learning Method: From the Schrödinger Equation to an Orbital-Free-DFT Kinetic Energy Functional.. The journal of physical chemistry. A. https://doi.org/10.1021/acs.jpca.4c04155
Source records
- pubmed · retrieved 2026-11-10T11:54:00.416Z