title: Finding density functionals with machine learning.
abstract: Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with fewer than 100 training densities. A predictor identifies if a test density is within the interpolation region. Via principal component analysis, a projected functional derivative finds highly accurate self-consistent densities. The challenges for application of our method to real electronic structure problems are discussed.
authors.affiliation: Departments of Chemistry and of Physics, University of California, Irvine, California 92697, USA.
authors.forename: John C
authors.initials: JC
authors.lastname: Snyder
authors.forename: Matthias
authors.initials: M
authors.lastname: Rupp
authors.forename: Katja
authors.initials: K
authors.lastname: Hansen
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: Kieron
authors.initials: K
authors.lastname: Burke