Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica
Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica
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DOI：
10.1063/5.0055341
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发表时间：
2021-07-21
影响因子：
4.4
通讯作者：
Shiga, Motoyuki
中科院分区：
文献类型：
--
作者：
Kobayashi, Keita;Nagai, Yuki;Shiga, Motoyuki
自学习混合蒙特卡罗（SLHMC）是一种基于密度泛函理论的第一性原理模拟，它允许在势能表面上精确地生成系综。在机器学习潜力的帮助下，智能试验移动可以加速统计抽样。在第一份报告[Nagai et al., Phys。Rev. B 102, 041124(R)(2020)]，针对典型抽样的最简单情况引入了SLHMC方法。我们在此将这一想法扩展到等温-等压集成，以使软材料和体积波动大的液体的一般应用。利用等温-等压SLHMC方法研究了液态二氧化硅在接近熔点温度下的振动结构，其中缓慢的扩散运动超出了第一性原理分子动力学的时间尺度。结果表明，由第一性原理计算得到的静态结构因子与高能x射线数据吻合较好。
Self-learning hybrid Monte Carlo (SLHMC) is a first-principles simulation that allows for exact ensemble generation on potential energy surfaces based on density functional theory. The statistical sampling can be accelerated with the assistance of smart trial moves by machine learning potentials. In the first report [Nagai et al., Phys. Rev. B 102, 041124(R) (2020)], the SLHMC approach was introduced for the simplest case of canonical sampling. We herein extend this idea to isothermal-isobaric ensembles to enable general applications for soft materials and liquids with large volume fluctuation. As a demonstration, the isothermal-isobaric SLHMC method was used to study the vibrational structure of liquid silica at temperatures close to the melting point, whereby the slow diffusive motion is beyond the time scale of first-principles molecular dynamics. It was found that the static structure factor thus computed from first-principles agrees quite well with the high-energy x-ray data.