arXiv 2306.024062023-06-04cond-mat.mtrl-sciphysics.chem-ph

通过热力学积分获得第一性原理熔盐相图

First-principles molten salt phase diagrams through thermodynamic integration

Tanooj Shah, Kamron Fazel, Jie Lian, Liping Huang, Yunfeng Shi, Ravishankar Sundararaman

AI总结：

本研究提出从低成本力场向DFT训练的神经网络势进行热力学积分，以近经典势成本实现NaCl固-液相边界的第一性原理预测，并证明色散相互作用对准确性至关重要。

摘要：

在分子动力学（MD）模拟中精确预测相图颇具挑战，因为同时需要长时间尺度、大长度尺度和准确的原子间势。我们表明，从低成本力场到利用密度泛函理论（DFT）训练的神经网络势（NNP）进行热力学积分（TI），能够在模型盐NaCl中快速实现固-液相边界的第一性原理预测。我们使用该技术比较了若干DFT交换-关联泛函在预测NaCl相边界方面的准确性，并发现纳入色散相互作用对于获得与实验的良好一致性至关重要。重要的是，我们的方法引入了一种以从头算精度预测任意材料固-液相边界的途径，而大部分计算成本仅处于经典势的水平。

英文摘要：

Precise prediction of phase diagrams in molecular dynamics (MD) simulations is challenging due to the simultaneous need for long time scales, large length scales and accurate interatomic potentials. We show that thermodynamic integration (TI) from low-cost force fields to neural network potentials (NNPs) trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid-liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange-correlation functionals for predicting the NaCl phase boundary, and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid-liquid phase boundaries for any material at an ab-initio level of accuracy, with the majority of the computational cost at the level of classical potentials.

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