精确phase diagrams热力学积分的贝叶斯学习和数值收敛
Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams
摘要：
从分子动力学精确计算相图需要系统的处理和统计平均值的收敛。在这项工作中，我们提出了一个基于高斯过程回归的框架，用于使用各种来源的数据重建自由能函数。我们的框架允许传播统计不确定性从有限的分子动力学轨迹的相图和自动执行收敛相对于模拟参数。此外，我们的方法提供了一种方法，自动优化抽样的仿真参数空间的基础上贝叶斯优化方法。我们通过构建两个模型系统，Lennard-Jones和软核势的相图来验证我们的方法，并将结果与现有的工程研究和我们的共存模拟进行比较。最后，我们构建了温度高于300 K，压力低于30 GPa的锂相图，该相图来自于在从头算数据上训练的机器学习势。我们的方法表现良好的共存模拟和实验结果相比。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
