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.
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 is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 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.
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