High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark-Reference-Cited by-同舟云学术

High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark

Published:2022 Issue:48 Volume:24 Page:29381-29392
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Shanavas Rasheeda Dilshana¹^ORCID,Martín Santa Daría Alberto²^ORCID,Schröder Benjamin³^ORCID,Mátyus Edit²^ORCID,Behler Jörg¹^ORCID

Affiliation:

1. Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraβe 6, 37077 Göttingen, Germany

2. ELTE, Eötvös Loránd University, Institute of Chemistry, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary

3. Universität Göttingen, Institut für Physikalische Chemie, Tammannstraβe 6, 37077 Göttingen, Germany

Abstract

A high-dimensional neural network potential has been developed to study the harmonic and anharmonic frequencies of the formic acid dimer with coupled cluster accuracy.

Funder

Deutsche Forschungsgemeinschaft

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CP/D2CP03893E

Reference109 articles.

1. Machine learning for molecular and materials science

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