Issue 34, 2023
From the journal:

Physical Chemistry Chemical Physics

Themed collection on Insightful Machine Learning for Physical Chemistry

Aurora E. Clark, ORCID logo *a   Pavlo O. Dral, ORCID logo *b   Isaac Tamblyn ORCID logo *cd  and  Olexandr Isayev ORCID logo *e  

* 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

Abstract

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Graphical abstract: Themed collection on Insightful Machine Learning for Physical Chemistry

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Article information

DOI
https://doi.org/10.1039/D3CP90129G
Article type
Editorial
First published
13 júl 2023

Phys. Chem. Chem. Phys., 2023,25, 22563-22564

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Themed collection on Insightful Machine Learning for Physical Chemistry

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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