A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids-Reference-Cited by-同舟云学术

A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids

Published:2022-09-23 Issue:39 Volume:126 Page:16710-16720
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Cordova Manuel¹²^ORCID,Engel Edgar A.³,Stefaniuk Artur¹,Paruzzo Federico¹,Hofstetter Albert¹,Ceriotti Michele²⁴^ORCID,Emsley Lyndon¹²^ORCID

Affiliation:

1. Laboratory of Magnetic Resonance, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland

2. National Centre for Computational Design and Discovery of Novel Materials MARVEL, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland

3. Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.

4. Laboratory of Computational Science and Modelling, Institute of Materials, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland

Funder

Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung

Trinity College, University of Cambridge

Publisher

American Chemical Society (ACS)

Subject

Surfaces, Coatings and Films,Physical and Theoretical Chemistry,General Energy,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.2c03854

Reference65 articles.