There has been growing interest in the application of machine learning (ML) to the design of heterogeneous catalysts, including nanoparticle catalysts (NC) and single-atom catalysts (SACs). In this Review, the authors summarize recent advances in the ML-guided design of NCs and SACs for energy applications, focusing on the selection of features and descriptors for ML models from atom-scale structural information and identifying challenges and opportunities for the development of next-generation SACs through reliable datasets and advanced ML models.
- Zhongbao Hu
- Zhoukun Wang
- Jian-Feng Li