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Status: Unchecked ×Keyword: density functionals ×Clear all

2 claims from 1 paper

1. Materials Science › Machine Learning in Materials Science

   Finding Density Functionals with Machine Learning

   Snyder, Rupp, Hansen, Müller and Burke · Physical Review Letters · 2012

   The paper uses machine learning to approximate density functionals, testing it on a one-dimensional model problem and discussing the challenges of applying it to real electronic structure problems.

   Unchecked2 claims
   Show 2 claims

   1. UncheckedFor a 1d model of non-interacting fermions' kinetic energy, machine learning reached errors under 1 kcal/mol with fewer than 100 training densities.“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.”
   2. UncheckedIn a 1D model of non-interacting fermions, a projected functional derivative using principal component analysis finds highly accurate self-consistent densities.“Via principal component analysis, a projected functional derivative finds highly accurate self-consistent densities.”