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

Autor(es)

Gomes-Filho, Marcio S. ; Torres, Alberto ; Rocha, Alexandre Reily ; Pedroza, Luana S.

• Tipo de documento: Artigo Científico
• Fonte: Journal of Physical Chemistry B; v. 127, n. 6, p. 7-pg., 2023-02-16.

Resumo

Molecular 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. (AU)

• Processo FAPESP: 17/10292-0 - Simulações atomísticas em eletroquímica
• Beneficiário: Luana Sucupira Pedroza
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores
• Processo FAPESP: 20/09011-9 - Modelagem teórica de interfaces eletroquímicas
• Beneficiário: Márcio Sampaio Gomes Filho
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 16/01343-7 - ICTP Instituto Sul-Americano para Física Fundamental: um centro regional para física teórica
• Beneficiário: Nathan Jacob Berkovits
• Modalidade de apoio: Auxílio à Pesquisa - Projetos Especiais
• Processo FAPESP: 17/02317-2 - Interfaces em materiais: propriedades eletrônicas, magnéticas, estruturais e de transporte
• Beneficiário: Adalberto Fazzio
• Modalidade de apoio: Auxílio à Pesquisa - Temático