OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
Paper • 2107.00299 • Published
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Dataset Details
Dataset Description
Structures (including conformers, protomers, ...) of structures. Has been used for training of OrbNet Denali.
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- License: CC BY 4.0
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Citation
BibTeX:
@article{Christensen_2021,
doi = {10.1063/5.0061990},
url = {https://doi.org/10.1063%2F5.0061990},
year = 2021,
month = {nov},
publisher = {{AIP} Publishing},
volume = {155},
number = {20},
author = {Anders S. Christensen and Sai Krishna Sirumalla and Zhuoran Qiao and Michael B. O'Connor and Daniel G. A. Smith and Feizhi Ding and Peter J. Bygrave and Animashree Anandkumar and Matthew Welborn and Frederick R. Manby and Thomas F. Miller},
title = {{OrbNet} Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and {DFT} accuracy},
journal = {The Journal of Chemical Physics}
}
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