Kevin Ryczko
Kevin Ryczko
Verified email at uottawa.ca - Homepage
Title
Cited by
Cited by
Year
Deep learning and density-functional theory
K Ryczko, DA Strubbe, I Tamblyn
Physical Review A 100 (2), 022512, 2019
512019
Convolutional neural networks for atomistic systems
K Ryczko, K Mills, I Luchak, C Homenick, I Tamblyn
Computational Materials Science 149, 134-142, 2018
412018
Extensive deep neural networks for transferring small scale learning to large scale systems
K Mills, K Ryczko, I Luchak, A Domurad, C Beeler, I Tamblyn
Chemical science 10 (15), 4129-4140, 2019
262019
Crystal site feature embedding enables exploration of large chemical spaces
H Choubisa, M Askerka, K Ryczko, O Voznyy, K Mills, I Tamblyn, ...
Matter 3 (2), 433-448, 2020
132020
Hashkat: large-scale simulations of online social networks
K Ryczko, A Domurad, N Buhagiar, I Tamblyn
Social Network Analysis and Mining 7 (1), 4, 2017
102017
Structural characterization of water-metal interfaces
K Ryczko, I Tamblyn
Physical Review B 96 (6), 064104, 2017
52017
Inverse design of a graphene-based quantum transducer via neuroevolution
K Ryczko, P Darancet, I Tamblyn
The Journal of Physical Chemistry C 124 (48), 26117-26123, 2020
32020
Twin Neural Network Regression
SJ Wetzel, K Ryczko, RG Melko, I Tamblyn
arXiv preprint arXiv:2012.14873, 2020
22020
Orbital-Free Density Functional Theory with Small Datasets and Deep Learning
K Ryczko, SJ Wetzel, RG Melko, I Tamblyn
arXiv preprint arXiv:2104.05408, 2021
12021
Electric ion dispersion as a new type of mass spectrometer
M Lindstrom, I Moyles, K Ryczko
Mathematics-in-Industry Case Studies 7 (1), 1-13, 2017
12017
Electronic Response Quantities of Solids and Deep Learning
K Ryczko, O Malenfant-Thuot, M Côté, I Tamblyn
arXiv preprint arXiv:2108.07614, 2021
2021
Machine Learned Deep Neural Networks to Simulate Raman Spectrum of Defective Graphene Systems
M Cote, O Malenfant-Thuot, K Ryczko, A Majumdar, I Tamblyn
ECS Meeting Abstracts, 604, 2021
2021
Machine Learned Predictions of Complex Quantities from Differentiable Networks
O Malenfant-Thuot, K Ryczko, I Tamblyn, M Cote
Bulletin of the American Physical Society, 2021
2021
Neural evolution structure generation: High Entropy Alloys
CGT Feugmo, K Ryczko, A Anand, CV Singh, I Tamblyn
arXiv preprint arXiv:2103.01462, 2021
2021
Neural evolution structure generation: High Entropy Alloys
C Giresse Tetsassi Feugmo, K Ryczko, A Anand, C Veer Singh, I Tamblyn
arXiv e-prints, arXiv: 2103.01462, 2021
2021
Learning density functional theory mappings with extensive deep neural networks and deep convolutional inverse graphics networks
K Ryczko, D Strubbe, I Tamblyn
APS March Meeting Abstracts 2019, C18. 013, 2019
2019
Extensive deep neural networks for 2d materials
I Luchak, K Mills, K Ryczko, A Domurad, C Beeler, I Tamblyn
APS March Meeting Abstracts 2018, R12. 001, 2018
2018
Structural characterizations of water-metal interfaces with large-scale first principles molecular dynamics
K Ryczko, I Tamblyn
APS March Meeting Abstracts 2017, K26. 002, 2017
2017
Effect of iron content on the catalytic activity of Fe-MnOx electrodeposited films in water oxidation
E Selinger, K Ryczko, G Lopinski, M Armandi, B Bonelli, I Tamblyn
APS March Meeting Abstracts 2017, Y25. 004, 2017
2017
Characterizing water-metal interfaces and machine learning potential energy surfaces
K Ryczko
University of Ontario Institute of Technology (Canada), 2017
2017
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Articles 1–20