Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning.
- DOI
- 10.1021/acs.jctc.1c00812
- Published
- 2022 Feb 8
- Container
- Journal of chemical theory and computation
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jctc.1c00812,
title = {Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning.},
author = {Ryczko K and Wetzel SJ and Melko RG and Tamblyn I},
year = {2022},
journal = {Journal of chemical theory and computation},
doi = {10.1021/acs.jctc.1c00812},
url = {https://doi.org/10.1021/acs.jctc.1c00812}
}RIS
TY - JOUR TI - Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning. AU - Ryczko K AU - Wetzel SJ AU - Melko RG AU - Tamblyn I PY - 2022 JO - Journal of chemical theory and computation DO - 10.1021/acs.jctc.1c00812 UR - https://doi.org/10.1021/acs.jctc.1c00812 ER -
APA
K, R., SJ, W., RG, M., & I, T. (2022). Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning.. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.1c00812
Source records
- pubmed · retrieved 2026-11-10T12:23:21.790Z