Accurate Calculation of Interatomic Forces with Neural Networks Based on a Generative Transformer Architecture

doi:10.1021/acs.jctc.4c01205

	Accurate Calculation of Interatomic Forces with Neural Networks Based on a Generative Transformer Architecture
	Lai, Juntao 1,2; Kan, Bowen 3; Wu, Yangjun 3; Fu, Qiang 1,2; Shang, Honghui 4; Li, Zhenyu 2,4; Yang, Jinlong 2,4
	2024-10-23
发表期刊	JOURNAL OF CHEMICAL THEORY AND COMPUTATION
ISSN	1549-9618
页码	10
摘要	Using neural networks to express electronic wave functions represents a new paradigm for solving the Schrodinger equation in quantum chemistry. For practical quantum chemistry simulations, one needs to know not only energies of molecules, but also accurate forces acting on constituent atoms. In this work, we achieve the accurate calculation of interatomic forces on QiankunNet, a platform that combines transformer-based deep neural networks with efficient batched autoregressive sampling. Our approach permits the application of the Hellmann-Feynman theorem to force calculations without introducing corrective Pulay terms. The results show that the calculated interatomic forces are in close agreement with those derived from the full configuration interaction method, irrespective of whether the system is a simple molecule or a strongly correlated electron system like a linear hydrogen chain. Furthermore, the calculated interatomic forces are employed for atomic relaxation in the torsional rotation process of ethylene, and the energy barrier obtained from the scanned potential energy surface is in excellent agreement with the experiment. Our work contributes to the application of artificial intelligence to broader quantum chemistry simulations, such as modeling challenging chemical transformations where electron correlations are difficult to describe.
DOI	10.1021/acs.jctc.4c01205
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[T2222026] ; National Natural Science Foundation of China[22273091] ; National Natural Science Foundation of China[22393913] ; National Natural Science Foundation of China[YSBR-054] ; CAS Project for Young Scientists in Basic Research[2021ZD0303302] ; Innovation Program for Quantum Science and Technology ; Chinese Academy of Sciences ; University of Science and Technology of China
WOS研究方向	Chemistry ; Physics
WOS类目	Chemistry, Physical ; Physics, Atomic, Molecular & Chemical
WOS记录号	WOS:001340156900001
出版者	AMER CHEMICAL SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39514
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Fu, Qiang; Shang, Honghui; Yang, Jinlong
作者单位	1.Univ Sci & Technol China, Sch Future Technol, Hefei 230026, Peoples R China 2.Univ Sci & Technol China, Hefei Natl Lab, Hefei 230088, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Univ Sci & Technol China, Key Lab Precis & Intelligent Chem, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Lai, Juntao,Kan, Bowen,Wu, Yangjun,et al. Accurate Calculation of Interatomic Forces with Neural Networks Based on a Generative Transformer Architecture[J]. JOURNAL OF CHEMICAL THEORY AND COMPUTATION,2024:10.
APA	Lai, Juntao.,Kan, Bowen.,Wu, Yangjun.,Fu, Qiang.,Shang, Honghui.,...&Yang, Jinlong.(2024).Accurate Calculation of Interatomic Forces with Neural Networks Based on a Generative Transformer Architecture.JOURNAL OF CHEMICAL THEORY AND COMPUTATION,10.
MLA	Lai, Juntao,et al."Accurate Calculation of Interatomic Forces with Neural Networks Based on a Generative Transformer Architecture".JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2024):10.