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physics.chem-ph2021★ 35 cited
Machine learning the derivative discontinuity of density-functional theory
Johannes Gedeon, Jonathan Schmidt, Matthew J. P. Hodgson +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 func…
physics.chem-ph2020★ 7 cited
Improving the exchange and correlation potential in density functional approximations through constraints
Timothy J. Callow, Benjamin J. Pearce, Tom Pitts +3
We review and expand on our work to impose constraints on the effective Kohn Sham (KS) potential of local and semi-local density functional approximations. In this work, we relax a…