Accelerating Many-Body Quantum Chemistry via Generative Transformer-Enhanced Configuration Interaction
文献类型：期刊论文
| 作者 | Kan, Bowen1,2; Shang, Honghui3 |
| 刊名 | JOURNAL OF CHEMICAL THEORY AND COMPUTATION
 |
| 出版日期 | 2025-12-09 |
| 卷号 | 21期号:23页码:11989-12000 |
| ISSN号 | 1549-9618 |
| DOI | 10.1021/acs.jctc.5c01429 |
| 英文摘要 | 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. |
| 资助项目 | University of Science and Technology of China[NA] ; National Natural Science Foundation of China[T2222026] |
| WOS研究方向 | Chemistry ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001621936500001 |
| 出版者 | AMER CHEMICAL SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/43079]  |
| 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.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, 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. |
入库方式： OAI收割
来源：计算技术研究所
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