NNQS-AFQMC: Neural Network Quantum States Enhanced Fermionic Quantum Monte Carlo

doi:10.1021/acs.jctc.5c01138

	NNQS-AFQMC: Neural Network Quantum States Enhanced Fermionic Quantum Monte Carlo
	Xiao, Zhi-Yu 1; Kan, Bowen 2,3; Ma, Huan 4; Zhao, Bowen 4; Shang, Honghui 4
	2025-10-14
发表期刊	JOURNAL OF CHEMICAL THEORY AND COMPUTATION
ISSN	1549-9618
卷号	21 期号:19 页码:9587-9600
摘要	We introduce an efficient approach to implement neural network quantum states (NNQS) as trial wave functions in auxiliary-field quantum Monte Carlo (AFQMC). NNQS are a recently developed class of variational ansatze capable of flexibly representing many-body wave functions, though they often incur a high computational cost during optimization. AFQMC, on the other hand, is a powerful stochastic projector approach for ground-state calculations, but it normally requires an approximate constraint via a trial wave function or trial density matrix, whose quality affects the accuracy. Recently, it has been shown (Xiao et al., arXiv2505.18519) that a broad class of highly correlated wave functions can be integrated into AFQMC through stochastic sampling techniques. In this work, we apply this approach and present a direct integration of NNQS with AFQMC, allowing NNQS to serve as high-quality trial wave functions for AFQMC with manageable computational cost. We test the NNQS-AFQMC method on the challenging nitrogen molecule (N2) at stretched geometries. Our results demonstrate that AFQMC with an NNQS trial wave function can attain near-exact total energies, highlighting the potential of AFQMC with NNQS to overcome longstanding challenges in strongly correlated electronic structure calculations. We also outline future research directions for improving this promising methodology.
DOI	10.1021/acs.jctc.5c01138
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 ; Supercomputing Center of the USTC ; Institute of Physics, Chinese Academy of Sciences
WOS研究方向	Chemistry ; Physics
WOS类目	Chemistry, Physical ; Physics, Atomic, Molecular & Chemical
WOS记录号	WOS:001585274300001
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/2XEOYT63/41651
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xiao, Zhi-Yu; Shang, Honghui
作者单位	1.Chinese Acad Sci, Inst Phys, Beijing 100080, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 4.Univ Sci & Technol China, State Key Lab Precis & Intelligent Chem, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Xiao, Zhi-Yu,Kan, Bowen,Ma, Huan,et al. NNQS-AFQMC: Neural Network Quantum States Enhanced Fermionic Quantum Monte Carlo[J]. JOURNAL OF CHEMICAL THEORY AND COMPUTATION,2025,21(19):9587-9600.
APA	Xiao, Zhi-Yu,Kan, Bowen,Ma, Huan,Zhao, Bowen,&Shang, Honghui.(2025).NNQS-AFQMC: Neural Network Quantum States Enhanced Fermionic Quantum Monte Carlo.JOURNAL OF CHEMICAL THEORY AND COMPUTATION,21(19),9587-9600.
MLA	Xiao, Zhi-Yu,et al."NNQS-AFQMC: Neural Network Quantum States Enhanced Fermionic Quantum Monte Carlo".JOURNAL OF CHEMICAL THEORY AND COMPUTATION 21.19(2025):9587-9600.