title: Orbital-free bond breaking via machine learning.
abstract: Using a one-dimensional model, we explore the ability of machine learning to approximate the non-interacting kinetic energy density functional of diatomics. This nonlinear interpolation between Kohn-Sham reference calculations can (i) accurately dissociate a diatomic, (ii) be systematically improved with increased reference data and (iii) generate accurate self-consistent densities via a projection method that avoids directions with no data. With relatively few densities, the error due to the interpolation is smaller than typical errors in standard exchange-correlation functionals.
authors: Departments of Chemistry and of Physics, University of California, Irvine, California 92697, USA.
authors: John C
authors: JC
authors: Snyder
authors: Institute of Pharmaceutical Sciences, ETH Zurich, 8093 Zürich, Switzerland.
authors: Matthias
authors: M
authors: Rupp
authors: Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany.
authors: Katja
authors: K
authors: Hansen
authors: Department of Chemistry, University of California, Irvine, California 92697, USA.
authors: Leo
authors: L
authors: Blooston
authors: Machine Learning Group, Technical University of Berlin, 10587 Berlin, Germany.
authors: Klaus-Robert
authors: KR
authors: Müller
authors: Departments of Chemistry and of Physics, University of California, Irvine, California 92697, USA.
authors: Kieron
authors: K
authors: Burke
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D011789:Quantum Theory