Journal
JOURNAL OF PHYSICAL CHEMISTRY A
Volume 124, Issue 48, Pages 10066-10074Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpca.0c06019
Keywords
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Funding
- National Natural Science Foundation of China [21688102, 21803066, 22003061]
- Chinese Academy of Sciences Pioneer Hundred Talents Program [KJ2340000031]
- National Key Research and Development Program of China [2016YFA0200604]
- Anhui Initiative in Quantum Information Technologies [AHY090400]
- Strategic Priority Research Program of Chinese Academy of Sciences [XDC01040100]
- Fundamental Research Funds for the Central Universities [WK2340000091]
- University of Science and Technology of China [KY2340000094, KY2340000103]
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The interpolative separable density fitting (ISDF) is an efficient and accurate low-rank decomposition method to reduce the high computational cost and memory usage of the Hartree-Fock exchange (HFX) calculations with numerical atomic orbitals (NAOs). In this work, we present a machine learning K-means clustering algorithm to select the interpolation points in ISDF, which offers a much cheaper alternative to the expensive QR factorization with column pivoting (QRCP) procedure. QRCF We implement this K-means-based ISDF decomposition to accelerate hybrid functional calculations with NAOs in the HONPAS package. We demonstrate that this method can yield a similar accuracy for both molecules and solids at a much lower computational cost. In particular, K-means can remarkably reduce the computational cost of selecting the interpolation points by nearly two orders of magnitude compared to QRCP, resulting in a speedup of -10 times for ISDF-based HFX calculations.
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