Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks

Author:

Kubečka Jakub1ORCID,Ayoubi Daniel1ORCID,Tang Zeyuan2ORCID,Knattrup Yosef1ORCID,Engsvang Morten1ORCID,Wu Haide1ORCID,Elm Jonas1ORCID

Affiliation:

1. Department of Chemistry, Aarhus University, Langelandsgade 140, 8000 Aarhus C, Denmark

2. Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, 8000 Aarhus C, Denmark

Abstract

We present the application of machine learning methods to alleviate the computational cost of quantum chemistry calculations required for modeling atmospheric molecular clusters.

Funder

H2020 European Research Council

Villum Fonden

Danmarks Grundforskningsfond

Danmarks Frie Forskningsfond

H2020 Marie Skłodowska-Curie Actions

Publisher

Royal Society of Chemistry (RSC)

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