1-10. Global Neural Network Potential for Material Simulation and Catalysis
Zhipan Liu
Department of Chemistry, Fudan University, Shanghai, China;
Abstract: While the underlying potential energy surface (PES) determines the structure and other properties of material, it has been frustrated to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of material PES. This lecture introduces our recent progress in SSW-NN method and its application in catalysis. We designed a “Global-to-Global”approach for material discovery by combining for the first time the global optimization method with neural network (NN) techniques. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. All these methods have been implemented in LASP software (www.lasphub.com). A number of important functional materials, in particular those for catalysis e.g. ZnCrO oxides, are utilized as the examples to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery and catalysis. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.
Zhipan Liu got Ph.D in 2003 from Queens Univ Belfast under supervision of Prof. Peijun Hu, and then did PostDoc with Professor David King in University of Cambridge. He returned to China in 2005 and is now a full professor in Department of Chemistry, Fudan University. He has published more than 150 research papers, including 24 in JACS. He was appointed as Senior Editor for J. Phys. Chem. A/B/C since 2017. Zhipan Liu’s research focuses on the reactivity prediction of chemical systems for energy storage and conversion. Novel theoretical methods, such as the stochastic surface walking global optimization (SSW) method and more recently global neural network method, were developed in the group to search for novel structures of material and to identify low energy pathways.
Email: zpliu@fudan.edu.cn