Journal
JOURNAL OF PHYSICAL CHEMISTRY A
Volume 124, Issue 35, Pages 7155-7165Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpca.0c03886
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Funding
- ONR [N00014-13-1-0338]
- Center Chemistry in Solution and at Interfaces (CSI) - DOE [DE-SC0019394]
- National Science Foundation of China [11871110]
- National Key Research and Development Program of China [2016YFB0201200, 2016YFB0201203]
- Beijing Academy of Artificial Intelligence (BAAI)
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We introduce the deep post Hartree-Fock (DeePHF) method, a machine learning-based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predicts the energy difference between results of highly accurate models such as the coupled cluster method and low accuracy models such as the Hartree-Fock (HF) method, using the ground-state electronic orbitals as the input. It preserves all the symmetries of the original high accuracy model. The added computational cost is less than that of the reference HF or DFT and scales linearly with respect to system size. We examine the performance of DeePHF on organic molecular systems using publicly available data sets and obtain the state-of-art performance, particularly on large data sets.
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