首页|机器学习加速氧化还原电位和酸度常数计算

机器学习加速氧化还原电位和酸度常数计算

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
氧化还原电位和酸度常数作为重要的物理化学性质被应用于分析能源材料重要指标值.为了实现能源材料的计算设计,发展计算电化学的方法,在复杂电化学环境下计算这些性质至关重要.近年来,利用计算电化学方法计算氧化还原电位和酸度常数已经受到了广泛的关注.然而,常用的计算方法如基于隐式溶剂化模型的小分子自由能计算,对于复杂溶剂化环境的处理非常有限.因此,基于第一性原理分子动力学(AIMD)的自由能计算被引入来描述复杂溶剂化环境中的溶质-溶剂相互作用.同时,基于AIMD的自由能计算方法已经被证实可以准确预测这些物理化学性质.然而,由于AIMD计算效率低且计算资源需求大,需要引入机器学习分子动力学(MLMD)加速计算.MLMD通过机器学习方法,构建模拟体系结构到第一性原理计算结果的一对一映射,可以在低成本下实现长时间尺度的AIMD.对于氧化还原电位和酸度常数计算,如何构建训练机器学习势函数模型所需的数据集至关重要.本文介绍了如何通过自动化工作流实现自由能计算势函数的自动化构建,通过机器学习分子动力学计算自由能并转化为对应的物理化学性质.
Automated Workflow for Redox Potentials and Acidity Constants Calculations from Machine Learning Molecular Dynamics
Redox potentials and acidity constants are key properties for evaluating the performance of energy materials. To achieve computational design of new generation of energy materials with higher performances, computing redox potentials and acidity constants with computational chemistry have attracted lots of attention. However, many works are done by using implicit solvation models, which is difficult to be applied to complex solvation environments due to hard parameterization. Recently, ab initio molecular dynamics (AIMD) has been applied to investigate real electrolytes with complex solvation. Furthermore, AIMD based free energy calculation methods have been established to calculate these physical chemical properties accurately. However, due to the low efficiency of ab initio calculations and the high computational costs, AIMD based free energy calculations are limited to systems with less than 1000 atoms. To solve the dilemma, machine learning molecular dynamics (MLMD) is introduced to accelerate the calculations. By using machine learning method to construct one-to-one mapping from structures to computed potential energies and atomic forces, molecular dynamics can be carried out with much low costs under ab initio accuracy. In order to achieve the MLMD based free energy calculation, a new scheme for machine learning potential (MLP) should be introduced to collect training datasets. By combining the free energy perturbation sampling method and concurrent learning scheme, the training datasets can be collected along the reaction's pathway (insertion of an electron or a proton) with high efficiency and the free energy calculations based on MLMD show good accuracy in comparison with AIMD simulation. This paper describes how to constructing machine learning potential for free energy calculation through the automated workflow, and how to use MLMD to compute accurate free energy differences and corresponding physical chemical properties.

Machine learning molecular dynamicsAutomated workflowComplex systemsFree energy calculation

王锋、程俊

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厦门大学化学化工学院,固体表面物理化学国家重点实验室,能源材料化学协同创新中心,福建 厦门 361005

福建省能源材料科学与技术创新实验室(IKKEM),福建 厦门 361005

厦门大学人工智能研究院,福建 厦门 361005

机器学习分子动力学 自动化工作流 复杂体系 自由能计算

国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目中央高校基本科研业务费专项资金AI4EC联合实验室基金AI4EC联合实验室基金

222253022199115121991150220210019216111320720220009RD2023100101RD2022070501

2024

10.13208/j.electrochem.2307181

电化学
中国化学会

电化学

CSTPCD北大核心
影响因子：0.263
ISSN：1006-3471
年,卷(期)：2024.30(2)
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