Zhang Dantong, Zhang Qi, Peng Chao, Long Zhi, Zhuang Guilin, Kramer Denis, Komarneni Sridhar, Zhi Chunyi, Xue Dongfeng
Multiscale Crystal Materials Research Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
College of Chemical Engineering, Zhejiang University of Technology, 18, Chaowang Road, Hangzhou, Zhejiang Province 310032, China.
iScience. 2023 Apr 8;26(5):106624. doi: 10.1016/j.isci.2023.106624. eCollection 2023 May 19.
Oxygen redox electrocatalysis is the crucial electrode reaction among new-era energy sources. The prerequisite to rationally design an ideal electrocatalyst is accurately identifying the structure-activity relationship based on the so-called descriptors which link the catalytic performance with structural properties. However, the quick discovery of those descriptors remains challenging. In recent, the high-throughput computing and machine learning methods were identified to present great prospects for accelerating the screening of descriptors. That new research paradigm improves cognition in the way of oxygen evolution reaction/oxygen reduction reaction activity descriptor and reinforces the understanding of intrinsic physical and chemical features in the electrocatalytic process from a multiscale perspective. This review summarizes those new research paradigms for screening multiscale descriptors, especially from atomic scale to cluster mesoscale and bulk macroscale. The development of descriptors from traditional intermediate to eigen feature parameters has been addressed which provides guidance for the intelligent design of new energy materials.
氧还原电催化是新时代能源中至关重要的电极反应。合理设计理想电催化剂的前提是基于所谓的描述符准确识别结构-活性关系，这些描述符将催化性能与结构性质联系起来。然而，快速发现这些描述符仍然具有挑战性。最近，高通量计算和机器学习方法被认为在加速描述符筛选方面具有巨大潜力。这种新的研究范式以析氧反应/氧还原反应活性描述符的方式提高了认知，并从多尺度角度加强了对电催化过程中内在物理和化学特征的理解。本文综述了筛选多尺度描述符的这些新研究范式，特别是从原子尺度到团簇中尺度和体相宏观尺度。文中还讨论了描述符从传统中间体到本征特征参数的发展，这为新能源材料的智能设计提供了指导。
