Prediction of transition state structures of gas-phase chemical reactions via machine learning
Crossref DOI link: https://doi.org/10.1038/s41467-023-36823-3
Published Online: 2023-03-01
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Choi, Sunghwan http://orcid.org/0000-0002-4330-7710
Text and Data Mining valid from 2023-03-01
Version of Record valid from 2023-03-01
Article History
Received: 19 September 2022
Accepted: 15 February 2023
First Online: 1 March 2023
Competing interests
: The author declares no competing interests.