分子动力学
电场
离子
离子电导率
化学物理
离子键合
电导率
材料科学
化学
计算化学
物理化学
电极
物理
有机化学
电解质
量子力学
作者
Kaoru Hisama,Gerardo Valadez Huerta,Michihisa Koyama
标识
DOI:10.1016/j.commatsci.2022.111955
摘要
Ionic transport under an electric field bias is the fundamental component of electrochemical devices and processes. A universal neural network potential with Bader charge prediction is integrated into a Langevin thermostat 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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