Deep Learning Based Prediction of Perovskite Lattice Parameters from Hirshfeld Surface Fingerprints
Author:
Affiliation:
1. Department of Materials Design and Innovation, University at Buffalo, Buffalo, New York 14260-1660, United States
Funder
National Science Foundation
Publisher
American Chemical Society (ACS)
Subject
General Materials Science,Physical and Theoretical Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.0c02201
Reference27 articles.
1. Lattice constant prediction of orthorhombic ABO3 perovskites using support vector machines
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4. Comment on “Prediction of lattice constant in cubic perovskites”
5. Examination of the influence of different variables on prediction of unit cell parameters in perovskites using counter-propagation artificial neural networks
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