Zheng Shisheng, Zhang Xi-Ming, Liu Heng-Su, Liang Ge-Hao, Zhang Si-Wang, Zhang Wentao, Wang Bingxu, Yang Jingling, Jin Xian'an, Pan Feng, Li Jian-Feng
College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, College of Electronic Science and Engineering, College of Physical Science and Technology, Institute of Artificial Intelligence, School of Mathematical Sciences, Xiamen University, Xiamen, China.
School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen, China.
Nat Commun. 2025 Mar 14;16(1):2542. doi: 10.1038/s41467-025-57824-4.
Understanding active phases across interfaces, interphases, and even within the bulk under varying external conditions and environmental species is critical for advancing heterogeneous catalysis. Describing these phases through computational models faces the challenges in the generation and calculation of a vast array of atomic configurations. Here, we present a framework for the automatic and efficient exploration of active phases. This approach utilizes a topology-based algorithm leveraging persistent homology to systematically sample configurations across diverse coordination environments and material morphologies. Simultaneously, efficient machine learning force fields enable rapid computations. We demonstrate the effectiveness of this framework in two systems: hydrogen absorption in Pd, where hydrogen penetrates subsurface layers and the bulk, inducing a "hex" reconstruction critical for CO electroreduction, explored through 50,000 sampled configurations; and the oxidation dynamics of Pt clusters, where oxygen incorporation renders the clusters less active during oxygen reduction reactions, investigated through 100,000 sampled configurations. In both cases, the predicted active phases and their impacts on catalytic mechanisms closely align with previous experimental observations, indicating that the proposed strategy can model complex catalytic systems and discovery active phases under specific environmental conditions.
了解在不同外部条件和环境物种下跨越界面、中间相甚至体相内部的活性相，对于推进多相催化至关重要。通过计算模型描述这些相面临着生成和计算大量原子构型的挑战。在此，我们提出了一个用于自动且高效探索活性相的框架。这种方法利用基于拓扑的算法，借助持久同调理论，系统地在不同配位环境和材料形态下对构型进行采样。同时，高效的机器学习力场实现了快速计算。我们在两个体系中展示了该框架的有效性：在钯中的氢吸收，其中氢穿透次表层和体相，引发对CO电还原至关重要的“六边形”重构，通过50000个采样构型进行探索；以及铂团簇的氧化动力学，其中氧的掺入使团簇在氧还原反应中活性降低，通过100000个采样构型进行研究。在这两种情况下，预测的活性相及其对催化机制的影响与先前的实验观察结果密切一致，表明所提出的策略能够对复杂催化体系进行建模，并在特定环境条件下发现活性相。
