Finding Density Functionals with Machine Learning

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.

# Machine Learning in Materials Science # Materials Science # Advanced Chemical Physics Studies # Physics and Astronomy # Model Reduction and Neural Networks

John C. Snyder, Matthias Rupp, Katja Hansen, et al. (2012). "Finding Density Functionals with Machine Learning". Physical Review Letters. https://doi.org/10.1103/physrevlett.108.253002