Ghasemi S Alireza, Kühne Thomas D
Dynamics of Condensed Matter and Center for Sustainable Systems Design, Chair of Theoretical Chemistry, Paderborn University, Warburger Str. 100, D-33098 Paderborn, Germany.
J Chem Phys. 2021 Feb 21;154(7):074107. doi: 10.1063/5.0037319.
A novel approach to find the fermionic non-interacting kinetic energy functional with chemical accuracy using machine learning techniques is presented. To that extent, we apply machine learning to an intermediate quantity rather than targeting the kinetic energy directly. We demonstrate the performance of the method for three model systems containing three and four electrons. The resulting kinetic energy functional remarkably accurately reproduces self-consistently the ground state electron density and total energy of reference Kohn-Sham calculations with an error of less than 5 mHa. This development opens a new avenue to advance orbital-free density functional theory by means of machine learning.
提出了一种利用机器学习技术以化学精度找到费米子非相互作用动能泛函的新方法。在此范围内，我们将机器学习应用于一个中间量，而不是直接针对动能。我们展示了该方法对包含三个和四个电子的三个模型系统的性能。所得的动能泛函以小于5毫哈的误差自洽地非常精确地再现了参考Kohn-Sham计算的基态电子密度和总能量。这一进展为通过机器学习推进无轨道密度泛函理论开辟了一条新途径。
