摘要
电化学中的理论计算模拟对于从原子水平理解电化学过程中的机制至关重要,它可以弥补许多实验上无法解释的现象,如果能在原子尺度上确定理解反应的活性中心,得到电极或电催化剂结构的演变过程,建立反应的微观机理,从根本上解决电极氧化和腐蚀的问题,提高电化学催化剂的活性和稳定性,从而设计更高效的电催化剂。然而,电化学的理论计算模拟中仍然存在诸多问题,例如,溶剂化效应的实现、电极/电解质(金属/溶液)界面之间合适的模拟模型和方法、电化学过程中的结构演化以及如何降低结构计算的计算代价等。在这里,我们回顾了电化学建模方法的最新进展以及我们小组通过使用修正的泊松-玻尔兹曼连续介质溶剂化模型模拟溶剂化效应对溶剂化效应和模型进行改进。同时为了减少计算代价,我们更关注机器学习在电化学模拟中的应用,主要分为两个部分,即通过快速对多种不同组分的能量进行计算并筛选出合适组分,但是无法得到实际的结构演变情况。另一个是通过快速结构取样得到不同组分不同的结构变化能够更为直观的获得结构的演变过程,从而揭示反应的机理。我们以本课题组开发的SSW-NN的方法为例,总结了基于机器学习的原子模拟在电化学方面的应用,介绍了SSW-NN,模拟电化学反应条件下电极和电催化剂的氧化和腐蚀,并阐明了催化剂结构的活性和稳定性。
Theoretical simulations of electrocatalysis are vital for understanding the mechanism of the electrochemical process at the atomic level.It can help to reveal the in-situ structures of electrode surfaces and establish the microscopic mechanism of electrocatalysis,thereby solving the problems such as electrode oxidation and corrosion.However,there are still many problems in the theoretical electrochemical simulations,including the solvation effects,the electric double layer,and the structural transformation of electrodes.Here we review recent advances of theoretical methods in electrochemical modeling,in particular,the double reference approach,the periodic continuum solvation model based on the modified Poisson-Boltzmann equation(CM-MPB),and the stochastic surface walking method based on the machine learning potential energy surface(SSW-NN).The case studies of oxygen reduction reaction by using CM-MPB and SSW-NN are presented.
作者
李吉利
李晔飞
刘智攀
Ji-Li Li;Ye-Fei Li;Zhi-Pan Liu(Collaborative Innovation Center of Chemistry for Energy Material,Key Laboratory of Computational Physical Science(Ministry of Education),Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials,Department of Chemistry,Fudan University,Shanghai 200433,China;Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules,Shanghai Institute of Organic Chemistry,Chinese Academy of Sciences,Shanghai 200032,China)
出处
《电化学》
CAS
CSCD
北大核心
2022年第2期59-73,共15页
Journal of Electrochemistry
基金
supported by the National Key Research and Development Program of China(2018YFA0208600)
the National Science Foundation of China(21773032,21972023,21533001,22022301,91545107,91745201)。