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Chin. Phys. B, 2021, Vol. 30(6): 060701    DOI: 10.1088/1674-1056/abeeed
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Differentiable programming and density matrix based Hartree-Fock method

Hong-Bin Ren(任宏斌)1,2, Lei Wang(王磊)1,3, and Xi Dai(戴希)4,†
1 Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
2 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
3 Songshan Lake Materials Laboratory, Dongguan 523808, China;
4 Department of Physics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon 999077, Hong Kong, China
Abstract  Differentiable programming is an emerging programming paradigm that allows people to take derivative of an output of arbitrary code snippet with respect to its input. It is the workhorse behind several well known deep learning frameworks, and has attracted significant attention in scientific machine learning community. In this paper, we introduce and implement a density matrix based Hartree-Fock method that naturally fits into the demands of this paradigm, and demonstrate it by performing fully variational ground state calculation on several representative chemical molecules.
Keywords:  differentiable programming      quantum chemistry  
Received:  04 February 2021      Revised:  13 March 2021      Accepted manuscript online:  16 March 2021
PACS:  07.05.Mh (Neural networks, fuzzy logic, artificial intelligence) (First-principles theory)  
Fund: Project supported by the Hong Kong Research Grants Council, China (Project No. GRF16300918), the National Key R&D Program of China (Grant Nos. 2016YFA0300603 and 2016YFA0302400), and the National Natural Science Foundation of China (Grant No. 11774398).
Corresponding Authors:  Xi Dai     E-mail:

Cite this article: 

Hong-Bin Ren(任宏斌), Lei Wang(王磊), and Xi Dai(戴希) Differentiable programming and density matrix based Hartree-Fock method 2021 Chin. Phys. B 30 060701

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