35 citations · 122 across the 9 of their papers we have counts for
9 papers
Addressing Ill-conditioning in Density Functional Theory for Reliable Machine Learning
L. Arnstein, J. Wetherell, R. Lawrence +2
In principle, machine learning (ML) can be used to obtain any electronic property of a many-body system from its electron density within density functional theory. However, some ph…
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…
Exact exchange-correlation potentials for calculating the fundamental gap with a fixed number of electrons
M. J. P. Hodgson, J. Wetherell, Emmanuel Fromager
Capturing the discontinuous shift by in the exact exchange-correlation (xc) potential is the standard proposal for calculating the fundamental gap, , from the Koh…
Insights into one-body density matrices using deep learning
Jack Wetherell, Andrea Costamagna, Matteo Gatti +1
The one-body reduced density matrix (1-RDM) of a many-body system at zero temperature gives direct access to many observables, such as the charge density, kinetic energy and occupa…
Accurate real-time evolution of electron densities and ground-state properties from generalized Kohn-Sham theory
M. J. P. Hodgson, J. Wetherell
The exact static and time-dependent Kohn-Sham (KS) exchange-correlation (xc) potential is extremely challenging to approximate as it is a local multiplicative potential that depend…
Advantageous nearsightedness of many-body perturbation theory contrasted with Kohn-Sham density functional theory
Jack Wetherell, Matthew Hodgson, Leopold Talirz +1
For properties of interacting electron systems, Kohn-Sham (KS) theory is often favored over many-body perturbation theory (MBPT) owing to its low computational cost. However, the e…