Kevin Ryczko

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	All	Since 2019
Citations	345	327
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Isaac TamblynBlock & University of Ottawa & University of WaterlooVerified email at uottawa.ca
Kyle Mills1QBitVerified email at uoit.net
Iryna LuchakUniversity of British ColumbiaVerified email at student.ubc.ca
David A StrubbeDepartment of Physics, University of California, MercedVerified email at ucmerced.edu
Chris BeelerUniversity of OttawaVerified email at uottawa.ca
Roger G. MelkoFaculty, University of Waterloo & Perimeter Institute; Affiliate, Vector InstituteVerified email at uwaterloo.ca
Sebastian Johann WetzelPerimeter InstituteVerified email at perimeterinstitute.ca
Oleksandr VoznyyAssistant Professor at University of TorontoVerified email at utoronto.ca
Mikhail AskerkaYale UniversityVerified email at aya.yale.edu
Hitarth ChoubisaUniversity of TorontoVerified email at mail.utoronto.ca
Edward SargentNorthwestern University & University of TorontoVerified email at northwestern.edu
Conrard Giresse TETSASSI FEUGMOAssistant Professor of Chemistry, University of WaterlooVerified email at uwaterloo.ca
Chandra Veer SinghProfessor of Materials Science & Engineering; University of TorontoVerified email at utoronto.ca
Abu AnandGraduate StudentVerified email at mail.utoronto.ca
Jaron T. KrogelStaff Scientist, Oak Ridge National LaboratoryVerified email at ornl.gov
Pierre DarancetScientist, Center for Nanoscale Materials, Argonne National LaboratoryVerified email at anl.gov
Iain MoylesAssociate Professor, York UniversityVerified email at yorku.ca
Michel CoteUniversite de MontrealVerified email at umontreal.ca
Olivier Malenfant-ThuotPhD Student, Université de MontréalVerified email at umontreal.ca
Barbara BonelliPolitecnico di TorinoVerified email at polito.it

Kevin Ryczko

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Inverse Design Machine Learning Materials Discovery Accelerated Property Prediction


Title Sort by citations Sort by year Sort by 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	108	2019
Convolutional neural networks for atomistic systems K Ryczko, K Mills, I Luchak, C Homenick, I Tamblyn Computational Materials Science 149, 134-142, 2018	57	2018
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	52	2019
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	41	2020
Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning K Ryczko, SJ Wetzel, RG Melko, I Tamblyn Journal of Chemical Theory and Computation 18 (2), 1122-1128, 2022	29	2022
Hashkat: large-scale simulations of online social networks K Ryczko, A Domurad, N Buhagiar, I Tamblyn Social Network Analysis and Mining 7, 1-13, 2017	19	2017
Machine Learning Diffusion Monte Carlo Energies K Ryczko, JT Krogel, I Tamblyn Journal of Chemical Theory and Computation, 2022	10	2022
Neural evolution structure generation: High entropy alloys CG Tetsassi Feugmo, K Ryczko, A Anand, CV Singh, I Tamblyn The Journal of Chemical Physics 155 (4), 044102, 2021	10	2021
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	8	2020
Structural characterization of water-metal interfaces K Ryczko, I Tamblyn Physical Review B 96 (6), 064104, 2017	5	2017
Twin neural network regression SJ Wetzel, K Ryczko, RG Melko, I Tamblyn Applied AI Letters 3 (4), e78, 2022	4	2022
Electric ion dispersion as a new type of mass spectrometer M Lindstrom, I Moyles, K Ryczko Mathematics-in-Industry Case Studies 7, 1-13, 2017	2	2017
Accelerated Organic Crystal Structure Prediction with Genetic Algorithms and Machine Learning A Kadan, K Ryczko, A Wildman, R Wang, A Roitberg, T Yamazaki Journal of Chemical Theory and Computation, 2023		2023
Accelerating the Computation and Design of Nanoscale Materials with Deep Learning K Ryczko University of Ottawa, 2021		2021
Electronic Response Quantities of Solids and Deep Learning K Ryczko, O Malenfant-Thuot, M Côté, I Tamblyn arXiv preprint arXiv:2108.07614, 2021		2021
(Invited) Machine Learned Deep Neural Networks to Simulate Raman Spectrum of Defective Graphene Systems M Cote, O Malenfant-Thuot, K Ryczko, A Majumdar, I Tamblyn Electrochemical Society Meeting Abstracts 239, 604-604, 2021		2021
Machine Learned Predictions of Complex Quantities from Differentiable Networks O Malenfant-Thuot, K Ryczko, I Tamblyn, M Cote APS March Meeting Abstracts 2021, B21. 007, 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

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