RACE: A Modular and Interpretable Radial–Angular Correlation Descriptor for Atomistic Machine Learning
- DOI
- 10.1021/acs.jctc.6c01111
- Published
- 2026-10-08
- Container
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jctc.6c01111,
title = {RACE: A Modular
and Interpretable Radial–Angular
Correlation Descriptor for Atomistic Machine Learning},
author = {Raphael M. Tromer and Jhionathan de Lima and Cristiano Francisco Woellner and Paola Ferreira Barbosa and Roberto Ventura Santos and Luiz Antonio Ribeiro Junior},
year = {2026},
journal = {Journal of Chemical Theory
and Computation},
doi = {10.1021/acs.jctc.6c01111},
url = {https://doi.org/10.1021/acs.jctc.6c01111}
}RIS
TY - JOUR TI - RACE: A Modular and Interpretable Radial–Angular Correlation Descriptor for Atomistic Machine Learning AU - Raphael M. Tromer AU - Jhionathan de Lima AU - Cristiano Francisco Woellner AU - Paola Ferreira Barbosa AU - Roberto Ventura Santos AU - Luiz Antonio Ribeiro Junior PY - 2026 JO - Journal of Chemical Theory and Computation DO - 10.1021/acs.jctc.6c01111 UR - https://doi.org/10.1021/acs.jctc.6c01111 ER -
APA
Tromer, R. M., Lima, J. D., Woellner, C. F., Barbosa, P. F., Santos, R. V., & Junior, L. A. R. (2026). RACE: A Modular and Interpretable Radial–Angular Correlation Descriptor for Atomistic Machine Learning. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.6c01111
Source records
- crossref · retrieved 2026-11-04T16:22:53.720Z