12k citations · 33.8k across the 125 of their papers we have counts for
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Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Li Li, Stephan Hoyer, Ryan Pederson +4
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectur…
Simple hydrogenic estimates for the exchange and correlation energies of atoms and atomic ions, with implications for density functional theory
Aaron D. Kaplan, Biswajit Santra, Puskar Bhattarai +8
Exact density functionals for the exchange and correlation energies are approximated in practical calculations for the ground-state electronic structure of a many-electron system.…
Density sensitivity of empirical functionals
Suhwan Song, Stefan Vuckovic, Eunji Sim +1
Empirical fitting of parameters in approximate density functionals is common. Such fits conflate errors in the self-consistent density with errors in the energy functional, but den…
Quantifying and understanding errors in molecular geometries
Stefan Vuckovic, Kieron Burke
Electronic structure calculations are ubiquitous in most branches of chemistry, but all have errors in both energies and equilibrium geometries. Quantifying errors in possibly doze…
Confirmation of the PPLB derivative discontinuity: Exact chemical potential at finite temperatures of a model system
Francisca Sagredo, Kieron Burke
The landmark 1982 paper of Perdew, Parr, Levy, and Balduz (often called PPLB) laid the foundation for our modern understanding of the role of the derivative discontinuity in densit…
Classical turning surfaces in solids: When do they occur, and what do they mean?
Aaron D. Kaplan, Stewart J. Clark, Kieron Burke +1
Classical turning surfaces of Kohn-Sham potentials, separating classically-allowed regions (CARs) from classically-forbidden regions (CFRs), provide a useful and rigorous approach…