Learning DFT

Peter Schmitteckert

doi:10.1140/epjs/s11734-021-00095-z

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arXiv · 2008.07923

Learning DFT

Peter Schmitteckert

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Abstract

We present an extension of reverse engineered Kohn-Sham potentials from a density matrix renormalization group calculation towards the construction of a density functional theory functional via deep learning. Instead of applying machine learning to the energy functional itself, we apply these techniques to the Kohn-Sham potentials. To this end we develop a scheme to train a neural network to represent the mapping from local densities to Kohn-Sham potentials. Finally, we use the neural network to up-scale the simulation to larger system sizes.

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Peter Schmitteckert. 2020-08-18. Learning DFT. https://doi.org/10.1140/epjs/s11734-021-00095-z

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