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arXiv · 1112.5441

Finding Density Functionals with Machine Learning

John C. Snyder·Matthias Rupp·Katja Hansen·Klaus-Robert Müller·Kieron Burke
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Abstract

Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of non-interacting 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. Challenges for application of our method to real electronic structure problems are discussed.

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physics.comp-phcs.LGphysics.chem-phstat.ML

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John C. Snyder, Matthias Rupp, Katja Hansen, Klaus-Robert Müller, Kieron Burke. 2011-12-22. Finding Density Functionals with Machine Learning. https://doi.org/10.1103/physrevlett.108.253002

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