NNQS-AFQMC: Neural Network Quantum States Enhanced Fermionic Quantum Monte Carlo
文献类型：期刊论文
| 作者 | Xiao, Zhi-Yu1; Kan, Bowen2,3; Ma, Huan4; Zhao, Bowen4; Shang, Honghui4 |
| 刊名 | JOURNAL OF CHEMICAL THEORY AND COMPUTATION
 |
| 出版日期 | 2025-10-14 |
| 卷号 | 21期号:19页码:9587-9600 |
| ISSN号 | 1549-9618 |
| DOI | 10.1021/acs.jctc.5c01138 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:001585274300001 |
| 出版者 | AMER CHEMICAL SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/41650]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。