662 citations · 854 across the 5 of their papers we have counts for
6 papers
Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates
Justus T. C. Schwabedal, John C. Snyder, Ayse Cakmak +2
Randomizing the Fourier-transform (FT) phases of temporal-spatial data generates surrogates that approximate examples from the data-generating distribution. We propose such FT surr…
Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals
Kevin Vu, John Snyder, Li Li +5
Accurate approximations to density functionals have recently been obtained via machine learning (ML). By applying ML to a simple function of one variable without any random samplin…
Understanding Machine-learned Density Functionals
Li Li, John C. Snyder, Isabelle M. Pelaschier +6
Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one-dimensional box as a functional of their density. The properties of different…
Orbital-free Bond Breaking via Machine Learning
John C. Snyder, Matthias Rupp, Katja Hansen +3
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic…
Finding Density Functionals with Machine Learning
John C. Snyder, Matthias Rupp, Katja Hansen +2
Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of non-interacting fermions in 1d, mean absolute errors below 1 kcal/mol on…
Ionization potentials in the limit of large atomic number
Lucian A. Constantin, John C. Snyder, John P. Perdew +1
By extrapolating the energies of non-relativistic atoms and their ions with up to 3000 electrons within Kohn-Sham density functional theory, we find that the ionization potential r…