Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System
Hong-Bin Ren1,2, Lei Wang1,3, and Xi Dai4*
1Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China 2School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China 3Songshan Lake Materials Laboratory, Dongguan 523808, China 4Department of Physics, Hong Kong University of Science and Technology, Kowloon 999077, Hong Kong, China
Abstract：Kinetic energy (KE) functional is crucial to speed up density functional theory calculation. However, deriving it accurately through traditional physics reasoning is challenging. We develop a generally applicable KE functional estimator for a one-dimensional (1D) extended system using a machine learning method. Our end-to-end solution combines the dimensionality reduction method with the Gaussian process regression, and simple scaling method to adapt to various 1D lattices. In addition to reaching chemical accuracy in KE calculation, our estimator also performs well on KE functional derivative prediction. Integrating this machine learning KE functional into the current orbital free density functional theory scheme is able to provide us with expected ground state electron density.
. [J]. 中国物理快报, 2021, 38(5): 50701-.
Hong-Bin Ren, Lei Wang, and Xi Dai. Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System. Chin. Phys. Lett., 2021, 38(5): 50701-.
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2021, Vol. 38 
