Accurate fundamental invariant-neural network representation of ab initio potential energy surfaces  

作　　者：Bina Fu Dong H.Zhang 

机构地区：[1]State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry,Dalian Institute of Chemical Physics,Chinese Academy of Sciences,Dalian 116023,China [2]Hefei National Laboratory,Hefei 230088,China [3]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《National Science Review》2023年第12期20-33,共14页国家科学评论（英文版）

基　　金：supported by the National Natural Science Foundation of China(22288201 and 22173099);the Innovation Program for Quantum Science and Technology(2021ZD0303305);the Dalian Innovation Support Program(2021RD05)。

摘　　要：Highly accurate potential energy surfaces are critically important for chemical reaction dynamics.The large number of degrees of freedom and the intricate symmetry adaption pose a big challenge to accurately representing potential energy surfaces(PESs)for polyatomic reactions.Recently,our group has made substantial progress in this direction by developing the fundamental invariant-neural network(FI-NN)approach.Here,we review these advances,demonstrating that the FI-NN approach can represent highly accurate,global,full-dimensional PESs for reactive systems with even more than 10 atoms.These multi-channel reactions typically involve many intermediates,transition states,and products.The complexity and ruggedness of this potential energy landscape present even greater challenges for full-dimensional PES representation.These PESs exhibit a high level of complexity,molecular size,and accuracy of fit.Dynamics simulations based on these PESs have unveiled intriguing and novel reaction mechanisms,providing deep insights into the intricate dynamics involved in combustion,atmospheric,and organic chemistry.

关 键 词：FI-NN full-dimensional PESs multi-channel reaction reaction mechanism 

分 类 号：O641.1[理学—物理化学] TP183[理学—化学]