Journal
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 12, Issue 3, Pages 1139-1147Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.5b01011
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Funding
- University of Notre Dame's College of Science and Department of Chemistry and Biochemistry
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We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from an input electron density. The output of the network is used as a nonlocal correction to conventional local and semilocal kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. The density which minimizes the total energy given by the functional is examined in detail. We identify several avenues to improve on this exploratory work, by reducing numerical noise and changing the structure of our functional. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.
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