Surrogate functionals for OF-DFT are defined and trained solely to make fixed density optimization reach the ground-state density, achieving competitive accuracy on QM9 and QMugs without O(N^3) orthonormalization.
Tieleman ,\ in\ @noop booktitle International Conference on Machine Learning \ ( year 2008 )\ pp.\ pages 1064--1071 NoStop
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Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory
Surrogate functionals for OF-DFT are defined and trained solely to make fixed density optimization reach the ground-state density, achieving competitive accuracy on QM9 and QMugs without O(N^3) orthonormalization.