Themed collection on Insightful Machine Learning for Physical Chemistry
Abstract
A graphical abstract is available for this content
- This article is part of the themed collection: Insightful Machine Learning for Physical Chemistry
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* Corresponding authors
a
Department of Chemistry, University of Utah, Salt Lake City, UT 84112, USA
E-mail:
aurora.clark@utah.edu
b
State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
E-mail:
dral@xmu.edu.cn
Web: https://dr-dral.com
c
Department of Physics, University of Ottawa, Canada
E-mail:
isaac.tamblyn@uottawa.ca
d Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
e
Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
E-mail:
olexandr@olexandrisayev.com
A graphical abstract is available for this content
A. E. Clark, P. O. Dral, I. Tamblyn and O. Isayev, Phys. Chem. Chem. Phys., 2023, 25, 22563 DOI: 10.1039/D3CP90129G
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