Publications¶
Kadan, Amit, Kevin Ryczko, Erika Lloyd, Adrian Roitberg, and Takeshi Yamazaki. “Guided multi-objective generative AI to enhance structure-based drug design.” Chemical Science (2025). [open access] [publication]
Malenfant-Thuot, Olivier, Kevin Ryczko, Isaac Tamblyn, and Michel Cote. “Efficient determination of Born-effective charges, LO-TO splitting, and Raman tensors of solids with a real-space atom-centered deep learning approach.” Journal of Physics: Condensed Matter 36, no. 42 (2024): 425901. [open access] [publication]
Kadan, Amit, Kevin Ryczko, Andrew Wildman, Rodrigo Wang, Adrian Roitberg, and Takeshi Yamazaki. “Accelerated organic crystal structure prediction with genetic algorithms and machine learning.” Journal of Chemical Theory and Computation 19, no. 24 (2023): 9388-9402. [open access] [publication]
Wetzel, Sebastian Johann, Kevin Ryczko, Roger Gordon Melko, and Isaac Tamblyn. “Twin neural network regression.” Applied AI Letters 3, no. 4 (2022): e78. [open access] [publication]
Ryczko, Kevin, Jaron T. Krogel, and Isaac Tamblyn. “Machine learning diffusion Monte Carlo energies.” Journal of Chemical Theory and Computation 18, no. 12 (2022): 7695-7701. [open access] [publication]
Ryczko, Kevin, Sebastian J. Wetzel, Roger G. Melko, and Isaac Tamblyn. “Toward orbital-free density functional theory with small data sets and deep learning.” Journal of Chemical Theory and Computation 18, no. 2 (2022): 1122-1128. [open access] [publication]
Ryczko, Kevin. “Accelerating the Computation and Design of Nanoscale Materials with Deep Learning.” PhD diss., Université d’Ottawa/University of Ottawa, 2021. [open access] [publication]
Ryczko, Kevin, Olivier Malenfant-Thuot, Michel Côté, and Isaac Tamblyn. “Electronic Response Quantities of Solids and Deep Learning.” arXiv preprint arXiv:2108.07614 (2021). [open access]
Tetsassi Feugmo, Conrard Giresse, Kevin Ryczko, Abu Anand, Chandra Veer Singh, and Isaac Tamblyn. “Neural evolution structure generation: High entropy alloys.” The Journal of Chemical Physics 155, no. 4 (2021). [open access] [publication]
Ryczko, Kevin, Pierre Darancet, and Isaac Tamblyn. “Inverse design of a graphene-based quantum transducer via neuroevolution.” The Journal of Physical Chemistry C 124, no. 48 (2020): 26117-26123. [open access] [publication]
Choubisa, Hitarth, Mikhail Askerka, Kevin Ryczko, Oleksandr Voznyy, Kyle Mills, Isaac Tamblyn, and Edward H. Sargent. “Crystal site feature embedding enables exploration of large chemical spaces.” Matter 3, no. 2 (2020): 433-448. [open access] [publication]
Ryczko, Kevin, David A. Strubbe, and Isaac Tamblyn. “Deep learning and density-functional theory.” Physical Review A 100, no. 2 (2019): 022512. [open access] [publication]
Mills, Kyle, Kevin Ryczko, Iryna Luchak, Adam Domurad, Chris Beeler, and Isaac Tamblyn. “Extensive deep neural networks for transferring small scale learning to large scale systems.” Chemical science 10, no. 15 (2019): 4129-4140. [open access] [publication]
Ryczko, Kevin, Kyle Mills, Iryna Luchak, Christa Homenick, and Isaac Tamblyn. “Convolutional neural networks for atomistic systems.” Computational Materials Science 149 (2018): 134-142. [open access] [publication]
Ryczko, Kevin, Adam Domurad, Nicholas Buhagiar, and Isaac Tamblyn. “Hashkat: large-scale simulations of online social networks.” Social Network Analysis and Mining 7, no. 1 (2017): 4. [open access] [publication]
Ryczko, Kevin, and Isaac Tamblyn. “Structural characterization of water-metal interfaces.” Physical Review B 96, no. 6 (2017): 064104. [open access] [publication]
Ryczko, Kevin. Characterizing water-metal interfaces and machine learning potential energy surfaces. University of Ontario Institute of Technology (Canada), 2017. [open access]
Lindstrom, Michael, Iain Moyles, and Kevin Ryczko. “Electric ion dispersion as a new type of mass spectrometer.” Mathematics-in-Industry Case Studies 7, no. 1 (2016): 1. [open access] [publication]
Ryczko, Kevin. “Modelling an Online Social Network.” PhD diss., University of Ontario Institute of Technology, 2014.