Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System

• 作者: Hong-Bin Ren Lei Wang Xi Dai
• 来源期刊: 中国物理快报（英文版）
• 年，卷（期）: 2021,（5）
• 所属期刊栏目: GENERAL
• 页码范围: 1-6
• 页数: 6页
• 语种: 英文
• DOI: 10.1088/0256-307X/38/5/050701

摘要

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.