计算机科学
相图
趋同(经济学)
统计物理学
数值积分
贝叶斯概率
应用数学
相(物质)
人工智能
物理
数学
数学分析
经济增长
量子力学
经济
作者
Vladimir Ladygin,I. Beniya,E. Makarov,Alexander V. Shapeev
出处
期刊:Physical review
[American Physical Society]
日期:2021-09-07
卷期号:104 (10)
被引量:7
标识
DOI:10.1103/physrevb.104.104102
摘要
Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regression based framework for reconstructing the free energy functions using data of various origin. Our framework allows for propagating statistical uncertainty from finite molecular dynamics trajectories to the phase diagram and automatically performing convergence with respect to simulation parameters. Furthermore, our approach provides a way for automatic optimal sampling in the simulation parameter space based on Bayesian optimization approach. We validate our methodology by constructing phase diagrams of two model systems, the Lennard-Jones and soft-core potential, and compare the results with existing works studies and our coexistence simulations. Finally, we construct the phase diagram of lithium at temperatures above 300 K and pressures below 30 GPa from a machine-learning potential trained on ab initio data. Our approach performs well when compared to coexistence simulations and experimental results.
科研通智能强力驱动
Strongly Powered by AbleSci AI