113 citations · 696 across the 23 of their papers we have counts for
23 papers
Machine learning configuration interaction for ab initio potential energy curves
J. P. Coe
The concept of machine learning configuration interaction (MLCI) [J. Chem. Theory Comput. 2018, 14, 5739], where an artificial neural network (ANN) learns on the fly to select impo…
Lattice Density-Functional Theory for Quantum Chemistry
J. P. Coe
We propose a lattice density-functional theory for {\it ab initio} quantum chemistry or physics as a route to an efficient approach that approximates the full configuration interac…
Machine Learning Configuration Interaction
J. P. Coe
We propose the concept of machine learning configuration interaction (MLCI) whereby an artificial neural network is trained on-the-fly to predict important new configurations in an…
Testing density-functional approximations on a lattice and the limits of the related Hohenberg-Kohn-type theorem
V. V. França, J. P. Coe, I. D'Amico
We present a metric-space approach to quantify the performance of density-functional approximations for interacting many-body systems and to explore the validity of the Hohenberg-K…
Uniqueness of density-to-potential mapping for fermionic lattice systems
J. P. Coe, I. D'Amico, V. V. França
We demonstrate that, for a fermionic lattice system, the ground-state particle density uniquely determines the external potential except for the sites corresponding to nodes of the…
Multireference X-Ray Emission and Absorption Spectroscopy calculations from Monte Carlo Configuration Interaction
J. P. Coe, M. J. Paterson
We adapt the method of Monte Carlo configuration interaction to calculate core-hole states and use this for the computation of X-ray emission and absorption values. We consider CO,…