LASP to the Future of Atomic Simulation:Intelligence and Automation  

作　　者：Xin-Tian Xie Zheng-Xin Yang Dongxiao Chen Yun-Fei Shi Pei-Lin Kang Sicong Ma Ye-Fei Li Cheng Shang Zhi-Pan Liu 

机构地区：[1]Collaborative Innovation Center of Chemistry for Energy Material,Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials,Key Laboratory of Computational Physical Science,Department of Chemistry,Fudan University,Shanghai 200433,China [2]State Key Laboratory of Metal Organic Chemistry,Shanghai Institute of Organic Chemistry,Chinese Academy of Sciences,Shanghai 200032,China

出　　处：《Precision Chemistry》2024年第12期612-627,共16页精准化学(英文)

基　　金：supported by the National Science Foundation of China(12188101,22033003,22122301,91945301,91745201,22203101);the Fundamental Research Funds for the Central Universities(20720220011);the National Key Research and Development Program of China(2018YFA0208600);Science&Technology Commission of Shanghai Municipality(23ZR1476100);the Tencent Foundation for XPLORER PRIZE.

摘　　要：Atomic simulations aim to understand and predict complex physical phenomena,the success of which relies largely on the accuracy of the potential energy surface description and the efficiency to capture important rare events.LASP software(large-scale atomic simulation with a Neural Network Potential),released in 2018,incorporates the key ingredients to fulfill the ultimate goal of atomic simulations by combining advanced neural network potentials with efficient global optimization methods.This review introduces the recent development of the software along two main streams,namely,higher intelligence and more automation,to solve complex material and reaction problems.The latest version of LASP(LASP 3.7)features the global many-body function corrected neural network(G-MBNN)to improve the PES accuracy with low cost,which achieves a linear scaling efficiency for large-scale atomic simulations.The key functionalities of LASP are updated to incorporate(i)the ASOP and ML-interface methods for finding complex surface and interface structures under grand canonic conditions;(ii)the ML-TS and MMLPS methods to identify the lowest energy reaction pathway.With these powerful functionalities,LASP now serves as an intelligent data generator to create computational databases for end users.We exemplify the recent LASP database construction in zeolite and the metal−ligand properties for a new catalyst design.

关 键 词：Machine learning Global neural network potential Large-scale atomic simulation Potential energy surface Software FIRST-PRINCIPLES Catalytic reactions Material design 

分 类 号：O64[理学—物理化学]