Accelerating Many-Body Quantum Chemistry via Generative Transformer-Enhanced Configuration Interaction

doi:10.1021/acs.jctc.5c01429

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	Accelerating Many-Body Quantum Chemistry via Generative Transformer-Enhanced Configuration Interaction
	Kan, Bowen 1,2; Shang, Honghui 3
	2025-12-09
发表期刊	JOURNAL OF CHEMICAL THEORY AND COMPUTATION
ISSN	1549-9618
卷号	21 期号:23 页码:11989-12000
摘要	Quantum many-body calculations are fundamentally limited by the exponential growth of the configuration space, making accurate treatment of strongly correlated systems computationally prohibitive. Here we present the Generative Transformer Neural Network Selected Configuration Interaction (GTNN-SCI), a Transformer-based machine learning approach that generatively samples important configurations to accelerate many-body quantum chemistry calculations. By leveraging the Transformer architecture's self-attention mechanism to capture long-range electron correlations, GTNN-SCI achieves up to 10x speedup compared to state-of-the-art neural network methods while maintaining high accuracy. We demonstrate the efficacy of GTNN-SCI by calculating correlation and binding energies for representative molecules including N2, H2O, and C2 using both Gaussian (cc-pVDZ) and plane-wave basis sets, achieving faster convergence and lower energies than previously reported neural network-based selected CI techniques. Most significantly, our generative approach identifies higher-order excitations missed by conventional coupling schemes, yielding lower variational energies than established methods including heat-bath CI. This capability enables GTNN-SCI to accurately treat the strongly correlated [Fe2S2(SCH3)4]2- ([2Fe-2S]) cluster system, achieving ground-state energies within chemical accuracy of DMRG benchmarks, whereas conventional selected CI methods have failed on this system. The GTNN-SCI method thus combines modern deep learning with high-performance electronic structure computation, providing an efficient and precise avenue for solving the electronic Schrodinger equation in challenging molecular systems.
DOI	10.1021/acs.jctc.5c01429
收录类别	SCI
语种	英语
WOS研究方向	Chemistry ; Physics
WOS类目	Chemistry, Physical ; Physics, Atomic, Molecular & Chemical
WOS记录号	WOS:001621936500001
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/2XEOYT63/43080
专题	中国科学院计算技术研究所
通讯作者	Shang, Honghui
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Univ Sci & Technol China, State Key Lab Precis & Intelligent Chem, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Kan, Bowen,Shang, Honghui. Accelerating Many-Body Quantum Chemistry via Generative Transformer-Enhanced Configuration Interaction[J]. JOURNAL OF CHEMICAL THEORY AND COMPUTATION,2025,21(23):11989-12000.
APA	Kan, Bowen,&Shang, Honghui.(2025).Accelerating Many-Body Quantum Chemistry via Generative Transformer-Enhanced Configuration Interaction.JOURNAL OF CHEMICAL THEORY AND COMPUTATION,21(23),11989-12000.
MLA	Kan, Bowen,et al."Accelerating Many-Body Quantum Chemistry via Generative Transformer-Enhanced Configuration Interaction".JOURNAL OF CHEMICAL THEORY AND COMPUTATION 21.23(2025):11989-12000.