Modeling microsolvation clusters with electronic-structure calculations guided by analytical potentials and predictive machine learning techniques-Reference-Cited by-同舟云学术

Modeling microsolvation clusters with electronic-structure calculations guided by analytical potentials and predictive machine learning techniques

Published:2021 Issue:2 Volume:23 Page:1738-1749
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Jesus W. S.¹²³⁴^ORCID,Prudente F. V.¹²³⁴^ORCID,Marques J. M. C.⁵⁶⁷⁸⁹^ORCID,Pereira F. B.¹⁰¹¹⁹¹²¹¹^ORCID

Affiliation:

1. Instituto de Física

2. Universidade Federal da Bahia

3. 40170-115 Salvador

4. Brazil

5. CQC

6. Department of Chemistry

7. University of Coimbra

8. 3004-535 Coimbra

9. Portugal

10. Coimbra Polytechnic – ISEC

11. Coimbra

12. Centro de Informática e Sistemas da Universidade de Coimbra (CISUC)

Abstract

A random-forest machine learning classifier promotes an efficiency enhancement in the DFT re-optimization of microsolvation clusters by selecting promising minimum structures that were searched by an evolutionary algorithm on an analytical PES.

Funder

Fundação para a Ciência e a Tecnologia

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2021/CP/D0CP05200K

Reference103 articles.

1. Infrared Spectroscopy of Size-Selected Benzene−Water Cluster Cations [C6H6−(H2O)n]+ (n = 1−23): Hydrogen Bond Network Evolution and Microscopic Hydrophobicity

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4. Ion Hydration and Ion Pairing as Probed by THz Spectroscopy

5. Global minima and energetics of Li+(H2O)n and Ca2+(H2O)n clusters for n⩽20

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