Professora Luana Pedroza – INCT – Institutos Nacionais de Ciência e Tecnologia

Publicações de Luana Pedroza

2024
Arvelos, Graciele M.; Fernández-Serra, Marivi; Rocha, Alexandre Reily; Pedroza, Luana S.
Probing Water-Electrified Electrode interfaces: Insights from Au and Pd Working paper
2024.
Resumo | Links | BibTeX | Tags:
@workingpaper{arvelos2024probingwaterelectrifiedelectrodeinterfaces,
title = {Probing Water-Electrified Electrode interfaces: Insights from Au and Pd},
author = {Graciele M. Arvelos and Marivi Fernández-Serra and Alexandre Reily Rocha and Luana S. Pedroza},
url = {https://arxiv.org/abs/2410.24150},
year = {2024},
date = {2024-10-31},
urldate = {2024-01-01},
abstract = {The water/electrode interface under an applied bias potential is a challenging out-of-equilibrium phenomenon, which is difficult to accurately model at the atomic scale. In this study, we employ a combined approach of Density Functional Theory (DFT) and non-equilibrium Green's function (NEGF) methods to analyze the influence of an external bias on the properties of water adsorbed on Au(111) and Pd(111) metallic electrodes. Our results demonstrate that while both Au and Pd-electrodes induce qualitatively similar structural responses in adsorbed water molecules, the quantitative differences are substantial, driven by the distinct nature of water-metal bonding. Our findings underscore the necessity of quantum-mechanical modeling for accurately describing electrochemical interfaces.},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}

Fechar
The water/electrode interface under an applied bias potential is a challenging out-of-equilibrium phenomenon, which is difficult to accurately model at the atomic scale. In this study, we employ a combined approach of Density Functional Theory (DFT) and non-equilibrium Green's function (NEGF) methods to analyze the influence of an external bias on the properties of water adsorbed on Au(111) and Pd(111) metallic electrodes. Our results demonstrate that while both Au and Pd-electrodes induce qualitatively similar structural responses in adsorbed water molecules, the quantitative differences are substantial, driven by the distinct nature of water-metal bonding. Our findings underscore the necessity of quantum-mechanical modeling for accurately describing electrochemical interfaces.
Fechar
https://arxiv.org/abs/2410.24150
Fechar
2023
Gomes-Filho, Márcio S.; Torres, Alberto; Rocha, Alexandre Reily; Pedroza, Luana S.
Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water Journal Article
Em: The Journal of Physical Chemistry B, vol. 127, não 6, pp. 1422-1428, 2023, (PMID: 36730848).
Resumo | Links | BibTeX | Tags:
@article{doi:10.1021/acs.jpcb.2c09059,
title = {Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water},
author = {Márcio S. Gomes-Filho and Alberto Torres and Alexandre Reily Rocha and Luana S. Pedroza},
url = {https://doi.org/10.1021/acs.jpcb.2c09059},
doi = {10.1021/acs.jpcb.2c09059},
year = {2023},
date = {2023-01-01},
journal = {The Journal of Physical Chemistry B},
volume = {127},
number = {6},
pages = {1422-1428},
abstract = {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.},
note = {PMID: 36730848},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Fechar
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.
Fechar
https://doi.org/10.1021/acs.jpcb.2c09059
doi:10.1021/acs.jpcb.2c09059
Fechar

Orientados e Supervisionados por Luana Pedroza

João Rheinheimer

Vínculo: Mestrado
Instituição: Universidade Federal do ABC (UFABC)
Projeto: Efeitos nucleares quânticos para moléculas absorvidas em superfícies metálicas. (FAPESP)

Currículo Lattes – ORCID – Google Scholar