发表机构
The University of Tokyo; Quemix Inc.; Idemitsu Kosan Co., Ltd.; National Institutes for Quantum Science and Technology (QST)（东京大学; Quemix公司; 出光兴产株式会社; 量子科学技术研究机构）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出OFDFT辅助的机器学习分子动力学方法，以低成本模拟电场驱动的离子传输，经β-Li₃PS₄块体及S₈/Li₃PS₄异质结构验证，可定性复现KSDFT结果，无需额外训练特定材料电荷预测模型。
AI 中文摘要
我们提出一种无轨道密度泛函理论（OFDFT）辅助的机器学习分子动力学方法，其中与电场无关的原子间力采用机器学习势计算，而依赖局部环境的原子电荷则通过OFDFT计算获得。通过对OFDFT电子密度进行巴德划分得到的原子电荷，与电场矢量相乘后，再叠加到机器学习势产生的力上。该方法能以比基于Kohn-Sham DFT（KSDFT）的分子动力学更低的计算成本，实现电场下的分子动力学模拟。作为概念验证，该方法被应用于周期性边界条件下的β-Li₃PS₄块体及S₈/Li₃PS₄异质结构。对于Li₃PS₄块体，OFDFT得到的Li电荷及PS₄单元总电荷与KSDFT结果一致；在异质结构中，Li离子在约170 ps的模拟时间内从Li₃PS₄区域迁移至富S区域，伴随该迁移，最初形成S₈环的S原子的平均巴德电荷从近中性变为负。对代表性界面结构与KSDFT的对比也证实，Li到达时S原子的负电荷状态及Li电荷的大小均得到定性复现。这些结果表明，使用通用机器学习势处理电场驱动的离子传输及界面电荷态变化是可行的，无需额外训练特定材料的电荷预测模型。
英文摘要
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.
