Differentiable programming and density matrix based Hartree–Fock method

作     者：Hong-Bin Ren Lei Wang Xi Dai 任宏斌;王磊;戴希

作者机构：Beijing National Laboratory for Condensed Matter Physics and Institute of PhysicsChinese Academy of SciencesBeijing 100190China School of Physical SciencesUniversity of Chinese Academy of SciencesBeijing 100049China Songshan Lake Materials LaboratoryDongguan 523808China Department of PhysicsHong Kong University of Science and TechnologyClear Water BayKowloon 999077Hong KongChina 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2021年第30卷第6期

页      面：249-254页

核心收录：

学科分类：07[理学] 0703[理学-化学] 

基　　金：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) the National Natural Science Foundation of China (Grant No. 11774398) 

主　　题：differentiable programming quantum chemistry 

摘      要：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.