Finding density functionals with machine learning

John C. Snyder, Matthias Rupp, Katja Hansen, Klaus Robert Müller, Kieron Burke

Research output: Contribution to journalArticlepeer-review

423 Citations (Scopus)


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 1kcal/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.

Original languageEnglish
Article number253002
JournalPhysical review letters
Issue number25
Publication statusPublished - 2012 Jun 19

ASJC Scopus subject areas

  • General Physics and Astronomy


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