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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 |
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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.
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Received: 04 February 2021
Revised: 13 March 2021
Accepted manuscript online: 16 March 2021
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PACS:
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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63.20.dk
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(First-principles theory)
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| 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: daix@ust.hk
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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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[1] Helgaker T, Jorgensen P and Olsen J 2014 Molecular electronic-structure theory (John Wiley and Sons)
[2] Boys S F 1950 Proc. R. Soc. Lond. Ser. A 200 542
[3] Hurley A C 1954 Proc. R. Soc. Lond. Ser. A 226 170
[4] Frost A A 1967 J. Chem. Phys. 47 3714
[5] Helgaker T and Almlöf J 1988 J. Chem. Phys. 89 4889
[6] Jensen F 1999 J. Chem. Phys. 110 6601
[7] Tachikawa M, Taneda K and Mori K 1999 Int. J. Quantum Chem. 75 497
[8] LeCun Y, Bengio Y and Hinton G 2015 Nature 521 436
[9] Baydin A G, Pearlmutter B A, Radul A A and Siskind J M 2017 The Journal of Machine Learning Research 18 5595
[10] Abadi M, Barham P, Chen J, et al. 2016 symposium on operating systems design and implementation pp. 265-283
[11] Revels J, Lubin M and Papamarkou T 2016 arXiv:1607.07892
[12] Innes M 2018 arXiv:1810.07951
[13] Tamayo-Mendoza T, Kreisbeck C, Lindh R and Aspuru-Guzik A 2018 ACS Cent. Sci. 4 559
[15] Helgaker T, Larsen H, Olsen J and Jorgensen P 2000 Chem. Phys. Lett. 327 397
[18] Hoai L T 2015 Product wave-functions in Fock-space (M.S. thesis) (German Research School for Simulation Sciences)
[16] McWeeny R 1960 Rev. Mod. Phys. 32 335
[17] Thouless D J 1960 Nucl. Phys. 21 225
[21] Sun Q, Berkelbach T C and Blunt N S, et al. 2018 Wiley Interdiscip. Rev. Comput. Mol. Sci. 8 e1340
[19] Kohn W and Sham L J 1965 Phys. Rev. 140 A1133
[20] Hohenberg P and Kohn W 1964 Phys. Rev. 136 B864 |
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