Molecular dynamics of electric-field driven ionic systems using a universal neural-network potential

K Hisama and GV Huerta and M Koyama, COMPUTATIONAL MATERIALS SCIENCE, 218, 111955 (2023).

DOI: 10.1016/j.commatsci.2022.111955

Ionic transport under an electric field bias is the fundamental component of electrochemical devices and pro-cesses. A universal neural network potential with Bader charge prediction is integrated into a Langevin ther-mostat NVT simulation to realize a direct simulation to examine those ion dynamics under an external electric field that is scalable and adaptable for various systems. We calculated the ion conductivity of O2-ions in yttria-stabilized zirconia (YSZ) and protons in hydrochloric acid water solution. The conductivity of YSZ shows a tendency consistent with the one using a Buckingham potential tuned for the system. For HClaq, the proton hopping contributes to the higher conductivity of protons than the counter anion Cl-, suggesting that our method is a promising tool for the ionic system, including chemical reactions and either for solid or liquid.

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