JACS Publishes ECUST New Progress of Machine Learning-assisted Structural Optimization of Catalytic Materials

Recently, the research group of Professor Wang Haifeng from the Institute of Industrial Catalysis/Computational Chemistry Center, School of Chemistry and Molecular Engineering, published an article entitled “Topology-Determined Structural Genes Enable Data-Driven Discovery and Intelligent Design of Potential Metal Oxides for Inert C–H Bond Activation” in the Journal of the American Chemical Society, a latest research result of the team in machine learning-assisted structural optimization of catalytic materials.

The article is co-first authored by Zhou Chuan and Chen Chen, Ph.D. students from School of Chemistry and Molecular Engineering, and is corresponding-authored by Professor Wang Haifeng. The research is financially supported by the State Key Laboratory of Green Chemical Industry and Industrial Catalysis, the State Key Laboratory of Structurally Controllable Advanced Functional Materials and Their Preparation, the State Key R&D Plan and the National Natural Science Foundation.