Accelerating Single Atom Catalyst Design Though Multiples scale modeling and Machine Learning Approaches
Using Grand-Canonical DFT, Kinetic Monte Carlo, and ML to study mechanism and design Single Atom Catalysts
In this project, we employ Grand-Canonical Density Functional Theory (DFT), Kinetic Monte Carlo simulations, and Machine Learning techniques to investigate the mechanisms and facilitate the design of Single Atom Catalysts (SACs). Our integrated approach offers valuable insights into the fundamental processes governing SAC behavior and paves the way for the development of high-performance catalysts for various applications
Fig. 1. Schematic of the SAC for CO2RR [1].
References
[1]. Shuyu Liang, Liang Huang, Yanshan Gao, Qiang Wang, Bin Liu, Adv. Sci.2021,8, 2102886