Machine learning the derivative discontinuity of density-functional theory

@article{Gedeon2021MachineLT,
  title={Machine learning the derivative discontinuity of density-functional theory},
  author={Johannes Gedeon and Jonathan Schmidt and M. Hodgson and Jack Wetherell and Carlos L Benavides-Riveros and Miguel A. L. Marques},
  journal={Machine Learning: Science and Technology},
  year={2021},
  volume={3}
}
Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned functionals are trained for systems with an integer number of particles. As such, they are unable to reproduce some crucial and fundamental aspects, such as the explicit dependency of the functionals on the particle number or the infamous derivative discontinuity at integer particle numbers. Here we propose a solution to… 

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