K
Kevin Ryczko
Researcher at University of Ottawa
Publications - 16
Citations - 272
Kevin Ryczko is an academic researcher from University of Ottawa. The author has contributed to research in topics: Artificial neural network & Density functional theory. The author has an hindex of 6, co-authored 13 publications receiving 180 citations. Previous affiliations of Kevin Ryczko include University of Ontario Institute of Technology.
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Deep learning and density-functional theory
TL;DR: In this paper, deep neural networks are integrated into the Kohn-Sham density functional theory (DFT) scheme for multielectron systems in simple harmonic oscillator and random external potentials with no feature engineering.
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Convolutional neural networks for atomistic systems
Kevin Ryczko,Kyle Mills,Iryna Luchak,Christa M. Homenick,Isaac Tamblyn,Isaac Tamblyn,Isaac Tamblyn +6 more
TL;DR: A new method, called CNNAS (convolutional neural networks for atomistic systems), for calculating the total energy of atomic systems which rivals the computational cost of empirical potentials while maintaining the accuracy of ab initio calculations is introduced.
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Extensive deep neural networks for transferring small scale learning to large scale systems
Kyle Mills,Kevin Ryczko,Iryna Luchak,Adam Domurad,Chris Beeler,Isaac Tamblyn,Isaac Tamblyn,Isaac Tamblyn +7 more
TL;DR: A physically-motivated topology of a deep neural network is presented that can efficiently infer extensive parameters of arbitrarily large systems, doing so with scaling.
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Crystal Site Feature Embedding Enables Exploration of Large Chemical Spaces
Hitarth Choubisa,Mikhail Askerka,Kevin Ryczko,Oleksandr Voznyy,Kyle Mills,Isaac Tamblyn,Edward H. Sargent +6 more
TL;DR: In this paper, the authors combine crystal site feature embedding (CSFE) representation with convolutional and extensive deep neural networks and achieve a low mean absolute test error of 3.7 meV/atom and 0.069 eV on density functional theory energies and band gaps of mixed halide perovskites.
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Hashkat: Large-scale simulations of online social networks
TL;DR: All of the algorithms and features integrated into hashkat are described before moving on to example use cases, which show how hashkat can be used to understand the underlying topology of social networks, validate sampling methods of such networks, and test new features of an online social network before going into production.