Reference : Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Dee...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Physics
Physics and Materials Science
http://hdl.handle.net/10993/44969
Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks
English
Stoehr, Martin mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS) >]
Medrano Sandonas, Leonardo mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS) >]
Tkatchenko, Alexandre mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS) >]
30-Jul-2020
Journal of Physical Chemistry Letters
American Chemical Society
11
16
6835–6843
Yes (verified by ORBilu)
International
1948-7185
1948-7185
Washington
DC
[en] We combine density-functional tight binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to construct a nonlinear model for the localized many-body interatomic repulsive energy, which so far has been treated in an atom-pairwise manner in DFTB. Substantially improving upon standard DFTB and DTNN, the resulting DFTB-NNrep model yields accurate predictions of atomization and isomerization energies, equilibrium geometries, vibrational frequencies, and dihedral rotation profiles for a large variety of organic molecules compared to the hybrid DFT-PBE0 functional. Our results highlight the potential of combining semiempirical electronic-structure methods with physically motivated machine learning approaches for predicting localized many-body interactions. We conclude by discussing future advancements of the DFTB-NNrep approach that could enable chemically accurate electronic-structure calculations for systems with tens of thousands of atoms.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/44969
10.1021/acs.jpclett.0c01307
https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.0c01307
H2020 ; 725291 - BeStMo - Beyond Static Molecules: Modeling Quantum Fluctuations in Complex Molecular Environments
FnR ; FNR11274975 > Martin Stöhr > > Coupling nuclear dynamics to electronic correlation in molecular materials > 01/10/2016 > 30/09/2020 > 2016

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