Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks-Reference-Cited by-同舟云学术

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

Published:2024 Issue: Volume: Page:
ISSN:2754-7000
Container-title:Environmental Science: Advances
language:en
Short-container-title:Environ. Sci.: Adv.

Author:

Kubečka Jakub¹^ORCID,Ayoubi Daniel¹^ORCID,Tang Zeyuan²^ORCID,Knattrup Yosef¹^ORCID,Engsvang Morten¹^ORCID,Wu Haide¹^ORCID,Elm Jonas¹^ORCID

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)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/VA/D4VA00255E

Reference82 articles.

1. Impact of nucleation on global CCN

2. The role of low-volatility organic compounds in initial particle growth in the atmosphere

3. Aeroparticles, Composition, and Lung Diseases

4. Early-life exposure to three size-fractionated ultrafine and fine atmospheric particulates in Beijing exacerbates asthma development in mature mice

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