Open AccessMachine Learning Approaches toward Orbital-free Density Functional Theory: Simultaneous Training on the Kinetic Energy Density Functional and Its Functional Derivative
Author(s) -
Ralf Meyer,
Manuel Weichselbaum,
Andreas Hauser
Publication year - 2020
Publication title -
journal of chemical theory and computation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.001
H-Index - 185
eISSN - 1549-9626
pISSN - 1549-9618
DOI - 10.1021/acs.jctc.0c00580
Subject(s) - density functional theory , computer science , orbital free density functional theory , functional derivative , energy functional , convolutional neural network , time dependent density functional theory , kernel (algebra) , minification , artificial intelligence , machine learning , hybrid functional , energy minimization , derivative (finance) , mathematical optimization , physics , mathematics , quantum mechanics , financial economics , economics , combinatorics , programming language
Orbital-free approaches might offer a way to boost the applicability of density functional theory by orders of magnitude in system size. An important ingredient for this endeavor is the kinetic energy density functional. Snyder et al. [ Phys. Rev. Lett. 2012, 108, 253002] presented a machine learning approximation for this functional achieving chemical accuracy on a one-dimensional model system. However, a poor performance with respect to the functional derivative, a crucial element in iterative energy minimization procedures, enforced the application of a computationally expensive projection method. In this work we circumvent this issue by including the functional derivative into the training of various machine learning models. Besides kernel ridge regression, the original method of choice, we also test the performance of convolutional neural network techniques borrowed from the field of image recognition.