等温等压系综的自学习混合Monte Carlo方法：在液态SiO2中的应用
Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica
摘要：
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方法，它允许在势能面上精确地产生系综。统计采样可以在机器学习潜力的智能尝试移动的帮助下加速。在第一份报告（Nagai，{ \it et al}. Phys.Rev.B102，041124（R）（2020）），SLHMC方法被引入用于典型采样的最简单情况。我们在这里扩展这个想法等温等压合奏，使软材料和液体的体积波动大的一般应用。作为示范，等温等压SLHMC方法被用来研究在接近熔点的温度下的液体二氧化硅的振动结构，其中缓慢的扩散运动是超出了第一原理分子动力学的时间尺度。结果发现，从第一性原理计算的静态结构因子与高能X射线数据吻合得很好。
英文摘要：
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, {\it 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.
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