Understanding machine-learned density functionals

Li Li, John C. Snyder, Isabelle M. Pelaschier, Jessica Huang, Uma Naresh Niranjan, Paul Duncan, Matthias Rupp, Klaus Robert Müller, Kieron Burke

Research output: Contribution to journalReview articlepeer-review

109 Citations (Scopus)


Machine learning (ML) is an increasingly popular statistical tool for analyzing either measured or calculated data sets. Here, we explore its application to a well-defined physics problem, investigating issues of how the underlying physics is handled by ML, and how self-consistent solutions can be found by limiting the domain in which ML is applied. The particular problem is how to find accurate approximate density functionals for the kinetic energy (KE) of noninteracting electrons. Kernel ridge regression is used to approximate the KE of non-interacting fermions in a one dimensional box as a functional of their density. The properties of different kernels and methods of cross-validation are explored, reproducing the physics faithfully in some cases, but not others. We also address how self-consistency can be achieved with information on only a limited electronic density domain. Accurate constrained optimal densities are found via a modified Euler-Lagrange constrained minimization of the machine-learned total energy, despite the poor quality of its functional derivative. A projected gradient descent algorithm is derived using local principal component analysis. Additionally, a sparse grid representation of the density can be used without degrading the performance of the methods. The implications for machine-learned density functional approximations are discussed.

Original languageEnglish
Pages (from-to)819-833
Number of pages15
JournalInternational Journal of Quantum Chemistry
Issue number11
Publication statusPublished - 2016 Jun 5


  • density functional theory
  • kinetic energy functional
  • machine learning
  • orbital free
  • self-consistent calculation

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Condensed Matter Physics
  • Physical and Theoretical Chemistry


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