Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal do ABC (UFABC)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorGomes-Filho, Márcio S.-
Autor(es): dc.creatorTorres, Alberto-
Autor(es): dc.creatorReily Rocha, Alexandre-
Autor(es): dc.creatorPedroza, Luana S.-
Data de aceite: dc.date.accessioned2025-08-21T19:37:48Z-
Data de disponibilização: dc.date.available2025-08-21T19:37:48Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-02-15-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1021/acs.jpcb.2c09059-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248312-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248312-
Descrição: dc.descriptionMolecular dynamics simulations have been used in different scientific fields to investigate a broad range of physical systems. However, the accuracy of calculation is based on the model considered to describe the atomic interactions. In particular, ab initio molecular dynamics (AIMD) has the accuracy of density functional theory (DFT) and thus is limited to small systems and a relatively short simulation time. In this scenario, Neural Network Force Fields (NNFFs) have an important role, since they provide a way to circumvent these caveats. In this work, we investigate NNFFs designed at the level of DFT to describe liquid water, focusing on the size and quality of the training data set considered. We show that structural properties are less dependent on the size of the training data set compared to dynamical ones (such as the diffusion coefficient), and a good sampling (selecting data reference for the training process) can lead to a small sample with good precision.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCentro de Ciências Naturais e Humanas Universidade Federal do ABC, São Paulo-
Descrição: dc.descriptionInstituto de Física Universidade de São Paulo-
Descrição: dc.descriptionInstitute of Theoretical Physics São Paulo State University-
Descrição: dc.descriptionInstitute of Theoretical Physics São Paulo State University-
Formato: dc.format1422-1428-
Idioma: dc.languageen-
Relação: dc.relationJournal of Physical Chemistry B-
???dc.source???: dc.sourceScopus-
Título: dc.titleSize and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.