电化学(中英文) ›› 2024, Vol. 30 ›› Issue (2): 2307181. doi: 10.13208/j.electrochem.2307181
所属专题: iSAIEC 2023
• 教程 • 上一篇
收稿日期:
2023-07-18
修回日期:
2023-10-04
接受日期:
2023-11-06
出版日期:
2024-02-28
发布日期:
2023-11-06
通讯作者:
*程俊,Tel: (86-592)2181570;E-mail: 基金资助:
Feng Wanga, Jun Chenga,b,c,*()
Received:
2023-07-18
Revised:
2023-10-04
Accepted:
2023-11-06
Published:
2024-02-28
Online:
2023-11-06
摘要:
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。
王锋, 程俊. 机器学习加速氧化还原电位和酸度常数计算[J]. 电化学(中英文), 2024, 30(2): 2307181.
Feng Wang, Jun Cheng. Automated Workflow for Redox Potentials and Acidity Constants Calculations from Machine Learning Molecular Dynamics[J]. Journal of Electrochemistry, 2024, 30(2): 2307181.
表1
通过自动化工作流获得的数据集大小和势函数对训练集的预测误差[31]
800, 800 | 0.553, 0.615 | 42.8, 44.2 | |
6016, 5234 | 0.653, 0.809 | 47.5, 50.8 | |
835, 835 | 0.580, 0.700 | 44.0, 45.7 | |
11048, 10648 | 0.556, 0.579 | 44.0, 43.4 | |
850, 827 | 0.544, 0.551 | 43.4, 45.5 | |
875, 873 | 0.532, 0.586 | 44.7, 48.1 | |
1996, 1996 | 0.797, 0.706 | 51.6, 49.5 | |
887, 887 | 0.722, 0.604 | 47.7, 47.8 | |
5499, 5499 | 0.819, 0.753 | 53.5, 52.1 | |
5791, 5791 | 0.733, 0.603 | 57.1, 47.7 |
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