【摘要】 随着能源需求增长与化石燃料资源枯竭之间的矛盾日益突出,以及石油、天然气等不可再生资源的燃烧带来的环境问题和全球变暖,清洁可再生能源越来越受到人们的重视.因此,包括能源转换和可逆能源使用等的可持续发展技术受到广泛关注.其中,电催化被认为是清洁能源转化的重要方法.目前,电催化反应的催化剂仍以贵金属为主.但贵金属昂贵的价格极大地限制了其使用,因此,开发廉价高效的电催化剂取代贵金属成为当务之急.传统的"试错法"费时费力且成本较高.近年来,随着超级计算机和计算理论的快速发展,密度泛函理论(DFT)和高通量计算可以指导材料的设计.尽管如此,要从巨大的化学空间中筛选出先进的电催化剂,使清洁能源技术得以广泛普及,仍是一个难题.幸运的是,跨学科融合及机器学习算法的发展为电催化剂的靶向设计注入新的动力.机器学习已经能够以接近DFT的计算精度模拟电化学过程.本文概述了机器学习方法在指导先进电催化剂的靶向设计中的应用,包括结构、热力学和动力学性质的推测、简单真空环境下的近似能量预测和考虑显式溶剂分子的复杂带电界面的模拟.除了基于机器学习的直接预测外,还介绍了通过拟合势能面或构造力场的方法来模拟催化反应中的动力学过程.在已有研究的基础上,展望了机器学习在固液界面模拟中的应用前景.本文提出的集成的机器学习模型有望有效地应用于恒电位下电催化界面的模拟,同时,用机器学习方法研究电化学过程将进一步推动高效电催化剂的靶向设计.更多还原

【Abstract】 Exploring the production and application of clean energy has always been the core of sustainable development. As a clean and sustainable technology, electrocatalysis has been receiving widespread attention. It is crucial to achieve efficient, stable and cheap electrocatalysts. However, the traditional “trial and error” method is time-consuming, laborious and costly. In recent years, with the significant increase in computing power, computations have played an important role in electrocatalyst design. Nevertheless, it is still difficult to search for advanced electrocatalysts in the vast chemical space through traditional density functional theory(DFT) computations. Fortunately, the development of machine learning and interdisciplinary integration will inject new impetus into targeted design of electrocatalysts. Machine learning is able to predict electrochemical performances with an accuracy close to DFT. Here we provide an overview of the application of machine learning in electrocatalyst design, including the prediction of structure, thermodynamic properties and kinetic barriers. We also discuss the potential of explicit solvent model combined with machine learning molecular dynamics in this field. Finally, the favorable circumstances and challenges are outlined for the future development of machine learning in electrocatalysis. The studies on electrochemical processes by machine learning will further realize targeted design of high-efficiency electrocatalysts.更多还原