4 papers
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…
Neural evolution structure generation: High Entropy Alloys
Conrard Giresse Tetsassi Feugmo, Kevin Ryczko, Abu Anand +2
We propose a method of neural evolution structures (NESs) combining artificial neural networks (ANNs) and evolutionary algorithms (EAs) to generate High Entropy Alloys (HEAs) struc…
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…
Deep Learning and Density Functional Theory
Kevin Ryczko, David Strubbe, Isaac Tamblyn
We show that deep neural networks can be integrated into, or fully replace, the Kohn-Sham density functional theory scheme for multi-electron systems in simple harmonic oscillator…