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Artificial neural network correction for density-functional tight-binding molecular dynamics simulations

Published online by Cambridge University Press:  28 June 2019

Junmian Zhu Open the ORCID record for Junmian Zhu [Opens in a new window] ,
Van Quan Vuong Open the ORCID record for Van Quan Vuong [Opens in a new window] ,
Bobby G. Sumpter Open the ORCID record for Bobby G. Sumpter [Opens in a new window]  and
Stephan Irle Open the ORCID record for Stephan Irle [Opens in a new window]
Junmian Zhu
Affiliation:
Department of Chemistry, Grinnell College, Grinnell, IA, USA
Van Quan Vuong
Affiliation:
Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, USA
Bobby G. Sumpter
Affiliation:
Center for Nanophase Materials Sciences and Computational Sciences & Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Stephan Irle*
Affiliation:
Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, USA Center for Nanophase Materials Sciences and Computational Sciences & Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
*
Address all correspondence to Stephan Irle at irles@ornl.gov
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Abstract

The authors developed a Behler–Parrinello-type neural network (NN) to improve the density-functional tight-binding (DFTB) energy and force prediction. The Δ-machine learning approach was adopted and the NN was designed to predict the energy differences between the density functional theory (DFT) quantum chemical potential and DFTB for a given molecular structure. Most notably, the DFTB-NN method is capable of improving the energetics of intramolecular hydrogen bonds and torsional potentials without modifying the framework of DFTB itself. This improvement enables considerably larger simulations of complex chemical systems that currently could not easily been accomplished using DFT or higher level ab initio quantum chemistry methods alone.

Information

Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 3 , September 2019 , pp. 867 - 873
Copyright
Copyright © Materials Research Society 2019 

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