Orbital-Free DFT-Assisted Machine-Learned Molecular Dynamics for Electric-Field-Driven Ionic Transport
Abstract
We propose an orbital-free density functional theory (OFDFT)-assisted machine-learned molecular dynamics method in which field-independent interatomic forces are evaluated using a machine-learned potential and atomic charges that depend on the local environment are obtained from OFDFT calculations. The atomic charges obtained by Bader partitioning of the OFDFT electron density are multiplied by the electric-field vector and added to the forces from the machine-learned potential. This approach enables molecular dynamics simulations under an electric field at a lower computational cost than Kohn-Sham DFT (KSDFT)-based molecular dynamics. As a proof of concept, the method was applied to beta-Li3PS4 bulk and an S8/Li3PS4 heterostructure under periodic boundary conditions. For Li3PS4 bulk, OFDFT yielded Li charges and a total charge of the PS4 unit consistent with those obtained using KSDFT. In the heterostructure, Li ions migrated from the Li3PS4 region into the S-rich region within a simulation time of approximately 170 ps. Accompanying this migration, the mean Bader charge of the S atoms that initially formed S8 rings changed from nearly neutral to negative. A comparison with KSDFT for a representative interfacial structure also confirmed that the negative charging of the S atoms upon the arrival of Li and the magnitude of the Li charges were qualitatively reproduced. These results demonstrate the possibility of treating field-driven ionic transport and changes in interfacial charge states using a universal machine-learned potential without training an additional material-specific charge-prediction model.
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BibTeX
@misc{nishiya2026orbitalfree,
title = {{Orbital-Free DFT-Assisted Machine-Learned Molecular Dynamics for Electric-Field-Driven Ionic Transport}},
author = {Yusuke Nishiya and Hiroya Nakata and Yosuke Harada and Yu-ichiro Matsushita},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.03518},
eprint = {2609.03518},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.03518},
}
RIS
TY - UNPB
TI - Orbital-Free DFT-Assisted Machine-Learned Molecular Dynamics for Electric-Field-Driven Ionic Transport
AU - Yusuke Nishiya
AU - Hiroya Nakata
AU - Yosuke Harada
AU - Yu-ichiro Matsushita
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2609.03518
UR - https://arxiv.org/abs/2609.03518
AB - We propose an orbital-free density functional theory (OFDFT)-assisted machine-learned molecular dynamics method in which field-independent interatomic forces are evaluated using a machine-learned potential and atomic charges that depend on the local environment are obtained from OFDFT calculations. The atomic charges obtained by Bader partitioning of the OFDFT electron density are multiplied by the electric-field vector and added to the forces from the machine-learned potential. This approach enables molecular dynamics simulations under an electric field at a lower computational cost than Kohn-Sham DFT (KSDFT)-based molecular dynamics. As a proof of concept, the method was applied to beta-Li3PS4 bulk and an S8/Li3PS4 heterostructure under periodic boundary conditions. For Li3PS4 bulk, OFDFT yielded Li charges and a total charge of the PS4 unit consistent with those obtained using KSDFT. In the heterostructure, Li ions migrated from the Li3PS4 region into the S-rich region within a simulation time of approximately 170 ps. Accompanying this migration, the mean Bader charge of the S atoms that initially formed S8 rings changed from nearly neutral to negative. A comparison with KSDFT for a representative interfacial structure also confirmed that the negative charging of the S atoms upon the arrival of Li and the magnitude of the Li charges were qualitatively reproduced. These results demonstrate the possibility of treating field-driven ionic transport and changes in interfacial charge states using a universal machine-learned potential without training an additional material-specific charge-prediction model.
ER -