23 citations · 44 across the 5 of their papers we have counts for
4 papers · 1 filter
Machine Learning Diffusion Monte Carlo Energies
Kevin Ryczko, Jaron T. Krogel, Isaac Tamblyn
We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small datasets (~60 DMC calculations in total). The first use…
Electronic Response Quantities of Solids and Deep Learning
Kevin Ryczko, Olivier Malenfant-Thuot, Michel Côté +1
We introduce a deep neural network (DNN) framework called the \textbf{r}eal-space \textbf{a}tomic \textbf{d}ecomposition \textbf{net}work (\textsc{radnet}), which is capable of mak…
Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning
Kevin Ryczko, Sebastian J. Wetzel, Roger G. Melko +1
We use voxel deep neural networks to predict energy densities and functional derivatives of electron kinetic energies for the Thomas-Fermi model and Kohn-Sham density functional th…
Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution
Kevin Ryczko, Pierre Darancet, Isaac Tamblyn
We introduce an inverse design framework based on artificial neural networks, genetic algorithms, and tight-binding calculations, capable to optimize the very large configuration s…