用神经演化势高效模拟铜磷硫化合物的结构与热性质
Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
- 基于第一性原理分子动力学数据训练神经演化势
- 新势函数计算速度提升41倍，精度接近密度泛函理论
- 适合研究铜基固态电解质在能源器件中的应用
Cu₇PS₆化合物因其在热电领域的潜力受到广泛关注。本研究引入神经演化势（NEP），基于从第一性原理分子动力学（AIMD）生成的数据集训练，以矩张量势（MTP）为参考。总能量和原子受力的均方根误差（RMSE）极低，表明MTP与NEP均具有高精度与良好可迁移性。利用两种机器学习势计算声子态密度（DOS）与径向分布函数（RDF），并与密度泛函理论（DFT）结果对比。尽管MTP精度略高，但NEP实现41倍的计算加速。研究揭示了微观动力学及快速铜离子扩散机制，为未来铜基固态电解质及其在能源器件中的应用提供重要依据。
原文摘要 · Abstract (English)
The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio molecular dynamics (AIMD) simulations, using the moment tensor potential (MTP) as a reference. The low root mean square errors (RMSEs) for total energy and atomic forces demonstrate the high accuracy and transferability of both the MTP and NEP. We further calculate the phonon density of states (DOS) and radial distribution function (RDF) using both machine learning potentials, comparing the results to density functional theory (DFT) calculations. While the MTP potential offers slightly higher accuracy, the NEP achieves a remarkable 41-fold increase in computational speed. These findings provide detailed microscopic insights into the dynamics and rapid Cu-ion diffusion, paving the way for future studies on Cu-based solid electrolytes and their applications in energy devices.
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