可解释性
力场(虚构)
计算机科学
领域(数学)
功能(生物学)
匹配(统计)
相(物质)
算法
采样(信号处理)
可微函数
势能
能量(信号处理)
统计物理学
相图
能量最小化
相变
电流(流体)
保守势力
力动力学
可视化
作者
Bin Jin,Bin Han,Wei Feng,Yu Kuang,Shenzhen Xu
标识
DOI:10.1021/acs.jctc.5c02032
摘要
Exact characterization of phase transitions requires sufficient configurational sampling, necessitating efficient and accurate potential energy surfaces. Molecular force fields with computational efficiency and physical interpretability are desirable but challenging to refine for complex interactions. To address this, we propose a force field refinement strategy with phase diagrams as top-down optimization targets based on automatic differentiation. Using gas-liquid coexistence as a paradigm, we employ an enhanced sampling technique and design a differentiable loss function to evaluate force fields' depiction of phase diagrams. The refined force fields produce gas-liquid phase diagrams matching well with targets for two modeling systems, moreover, phase-equilibrium chemical potentials are also automatically determined during the refinement process, which confirms our approach as an effective automated force field development framework for phase transition studies.
科研通智能强力驱动
Strongly Powered by AbleSci AI