ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification across the Periodic Table
- DOI
- 10.1021/acs.jcim.6c02178
- Published
- 2026-10-01
- Container
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jcim.6c02178,
title = {ElemeNet: Multiscale
Molecular Machine Learning with
Uncertainty Quantification across the Periodic Table},
author = {Jacob W. Toney and Samir Darouich and Yiran Wang and Aaron G. Garrison and Johannes Kästner and Heather J. Kulik},
year = {2026},
journal = {Journal of Chemical Information
and Modeling},
doi = {10.1021/acs.jcim.6c02178},
url = {https://doi.org/10.1021/acs.jcim.6c02178}
}RIS
TY - JOUR TI - ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification across the Periodic Table AU - Jacob W. Toney AU - Samir Darouich AU - Yiran Wang AU - Aaron G. Garrison AU - Johannes Kästner AU - Heather J. Kulik PY - 2026 JO - Journal of Chemical Information and Modeling DO - 10.1021/acs.jcim.6c02178 UR - https://doi.org/10.1021/acs.jcim.6c02178 ER -
APA
Toney, J. W., Darouich, S., Wang, Y., Garrison, A. G., Kästner, J., & Kulik, H. J. (2026). ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification across the Periodic Table. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.6c02178
Source records
- crossref · retrieved 2026-11-10T03:16:36.384Z