Zhang Mingcheng, Hou Yuchang, Jiang Yuzhu, Ni Xinyue, Wang Yanfei, Zou Xiaoxin
State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, 2699 Qianjin Street, Changchun 130012, China.
Key Laboratory of Functional Inorganic Material Chemistry (Ministry of Education), School of Chemistry and Materials Science, Heilongjiang University, 74 Xuefu Road, Harbin 150080, China.
Chem Commun (Camb). 2024 Dec 5;60(98):14521-14536. doi: 10.1039/d4cc05117c.
Electrocatalytic water splitting is vital for the sustainable production of green hydrogen. Electrocatalysts, including those for the hydrogen evolution reaction at the cathode and the oxygen evolution reaction at the anode, are crucial in determining the overall performance of water splitting. Traditional methods for electrocatalyst development often rely on trial-and-error, which can be time-consuming and inefficient. Recent advancements in computational techniques provide more systematic and predictive strategies for catalyst design. This review article explores the role of computational insights in the development of water-splitting electrocatalysts. We start by giving an introduction of electrocatalytic water splitting mechanisms. Then, fundamental theories such as the Sabatier principle and scaling relationships are reviewed, which provide a theoretical basis for catalytic activity. We also discuss thermodynamic, electronic, and geometric descriptors used to guide catalyst design and provide an in-depth discussion of their applications and limitations. Advanced computational approaches, including high-throughput screening, machine learning, solvation models and molecular dynamics, are also highlighted for their ability to accelerate catalyst discovery and simulate realistic reaction conditions. Finally, we propose future research directions aimed at searching universal descriptors, expanding data sets, and integrating developing interpretable models with catalyst design.
电催化水分解对于绿色氢能的可持续生产至关重要。电催化剂，包括用于阴极析氢反应和阳极析氧反应的催化剂，对于决定水分解的整体性能至关重要。传统的电催化剂开发方法通常依赖试错法，这可能既耗时又低效。计算技术的最新进展为催化剂设计提供了更系统和可预测的策略。这篇综述文章探讨了计算见解在水分解电催化剂开发中的作用。我们首先介绍电催化水分解机制。然后，回顾诸如萨巴蒂尔原理和标度关系等基础理论，这些理论为催化活性提供了理论基础。我们还讨论了用于指导催化剂设计的热力学、电子和几何描述符，并深入讨论了它们的应用和局限性。先进的计算方法，包括高通量筛选、机器学习、溶剂化模型和分子动力学，因其能够加速催化剂发现和模拟实际反应条件而受到关注。最后，我们提出了未来的研究方向，旨在寻找通用描述符、扩展数据集以及将可解释模型的开发与催化剂设计相结合。
