Abstract
This
Perspective explores the integration of machine learning potentials
(MLPs) in the research of heterogeneous catalysis, focusing on their
role in identifying in situ active sites and enhancing the
understanding of catalytic processes. MLPs utilize extensive databases
from high-throughput density functional theory (DFT) calculations to
train models that predict atomic configurations, energies, and forces
with near-DFT accuracy. These capabilities allow MLPs to handle
significantly larger systems and extend simulation times beyond the
limitations of traditional ab initio methods. Coupled with global
optimization algorithms, MLPs enable systematic investigations across
vast structural spaces, making substantial contributions to the modeling
of catalyst surface structures under reactive conditions. The review
aims to provide a broad introduction to recent advancements and
practical guidance on employing MLPs and also showcases several
exemplary cases of MLP-driven discoveries related to surface structure
changes under reactive conditions and the nature of active sites in
heterogeneous catalysis. The prevailing challenges faced by this
approach are also discussed.
X. Cheng, C. Wu, J. Xu, Y. Han, W. Xie, P. Hu, Leveraging machine learning potentials for in-situ searching of active sites in heterogeneous catalysis, Precision Chemistry, 2024. DOI: 10.1021/prechem.4c00051.
