论文标题
基于变异推断的概念抽样
Ab initio Canonical Sampling based on Variational Inference
论文作者
论文摘要
基于从头算分子动力学（AIMD）模拟的有限温度计算是一种强大的工具，能够预测无法从基态计算中推导的材料特性。但是，AIMD的高计算成本限制了其对大型或复杂系统的适用性。为了避免这种限制，我们引入了一种名为机器学习的新方法，辅助规范采样（MLACS），该采样加速了规范合奏中出生的 - oppenheimer势表面的采样。该方法基于自洽的变异程序，迭代训练机器学习间的潜在，以生成近似与Ab Inition势能能量相关的位置的规范分布的配置。通过证明该方法在Anharmonic Systems上的可靠性，我们表明该方法能够以其计算成本的一小部分来从头开始重现AIMD的结果。
Finite temperature calculations, based on ab initio molecular dynamics (AIMD) simulations, are a powerful tool able to predict material properties that cannot be deduced from ground state calculations. However, the high computational cost of AIMD limits its applicability for large or complex systems. To circumvent this limitation we introduce a new method named Machine Learning Assisted Canonical Sampling (MLACS), which accelerates the sampling of the Born--Oppenheimer potential surface in the canonical ensemble. Based on a self-consistent variational procedure, the method iteratively trains a Machine Learning Interatomic Potential to generate configurations that approximate the canonical distribution of positions associated with the ab initio potential energy. By proving the reliability of the method on anharmonic systems, we show that the method is able to reproduce the results of AIMD with an ab initio accuracy at a fraction of its computational cost.
