Finding density functionals with machine learning

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

Research output: Contribution to journalArticle

223 Citations (Scopus)

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 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
Volume108
Issue number25
DOIs
Publication statusPublished - 2012 Jun 19

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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    Snyder, J. C., Rupp, M., Hansen, K., Müller, K. R., & Burke, K. (2012). Finding density functionals with machine learning. Physical review letters, 108(25), [253002]. https://doi.org/10.1103/PhysRevLett.108.253002