
Publicações de Luana Pedroza
Mannino, Anthony; Arvelos, Graciele M.; Kaushik, Kedarsh; Artacho, Emilio; Ordejon, Pablo; Rocha, Alexandre R.; Pedroza, Luana S.; Fernández-Serra, Marivi First-Principles Nanocapacitor Simulations of the Optical Dielectric Constant in Water Ice Miscellaneous 2025. Resumo | Links | BibTeX | Tags: 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: 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: 2025
@misc{mannino2025firstprinciplesnanocapacitorsimulationsoptical,
title = {First-Principles Nanocapacitor Simulations of the Optical Dielectric Constant in Water Ice},
author = {Anthony Mannino and Graciele M. Arvelos and Kedarsh Kaushik and Emilio Artacho and Pablo Ordejon and Alexandre R. Rocha and Luana S. Pedroza and Marivi Fernández-Serra},
url = {https://arxiv.org/abs/2506.23003},
year = {2025},
date = {2025-06-28},
urldate = {2025-01-01},
abstract = {We introduce a combined density functional theory (DFT) and non-equilibrium Green's function (NEGF) framework to compute the capacitance of nanocapacitors and directly extract the dielectric response of a sub-nanometer dielectric under bias. We identify that at the nanoscale conventional capacitance evaluations based on stored charge per unit voltage suffer from an ill-posed partitioning of electrode and dielectric charge. This partitioning directly impacts the geometric definition of capacitance through the capacitor width, which in turn makes the evaluation of dielectric response uncertain. This ambiguous separation further induces spurious interfacial polarizability when analyzed via maximally localized Wannier functions. Focusing on crystalline ice, we develop a robust charge-separation protocol that yields unique capacitance-derived polarizability and dielectric constants, unequivocally demonstrating that confinement neither alters ice's intrinsic electronic response nor its insensitivity to proton order. Our results lay the groundwork for rigorous interpretation of capacitor measurements in low-dimensional dielectric materials.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
2024
@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}
}
2023
@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}
}
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)