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Isaac TamblynBlock & University of Ottawa & University of Waterloo在 uottawa.ca 的电子邮件经过验证
Kyle Mills1QBit在 uoit.net 的电子邮件经过验证
Iryna LuchakUniversity of Calgary在 ucalgary.ca 的电子邮件经过验证
David A StrubbeDepartment of Physics, University of California, Merced在 ucmerced.edu 的电子邮件经过验证
Chris BeelerUniversity of Ottawa在 uottawa.ca 的电子邮件经过验证
Roger G. MelkoFaculty, University of Waterloo & Perimeter Institute; Affiliate, Vector Institute在 uwaterloo.ca 的电子邮件经过验证
Sebastian Johann WetzelPerimeter Institute在 perimeterinstitute.ca 的电子邮件经过验证
Oleksandr VoznyyAssistant Professor at University of Toronto在 utoronto.ca 的电子邮件经过验证
Mikhail AskerkaYale University在 aya.yale.edu 的电子邮件经过验证
Hitarth ChoubisaUniversity of Toronto在 mail.utoronto.ca 的电子邮件经过验证
Edward SargentNorthwestern University & University of Toronto在 northwestern.edu 的电子邮件经过验证
Jaron T. KrogelStaff Scientist, Oak Ridge National Laboratory在 ornl.gov 的电子邮件经过验证
Conrard Giresse TETSASSI FEUGMOAssistant Professor of Chemistry, University of Waterloo在 uwaterloo.ca 的电子邮件经过验证
Chandra Veer SinghProfessor of Materials Science & Engineering; University of Toronto在 utoronto.ca 的电子邮件经过验证
Abu AnandGraduate Student在 mail.utoronto.ca 的电子邮件经过验证
Pierre DarancetScientist, Center for Nanoscale Materials, Argonne National Laboratory在 anl.gov 的电子邮件经过验证
Adrian RoitbergUniversity of Florida, Chemistry Department在 ufl.edu 的电子邮件经过验证
Iain MoylesAssociate Professor, York University在 yorku.ca 的电子邮件经过验证
Michel CoteUniversite de Montreal在 umontreal.ca 的电子邮件经过验证
Olivier Malenfant-ThuotPhD Student, Université de Montréal在 umontreal.ca 的电子邮件经过验证

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Inverse Design Machine Learning Materials Discovery Accelerated Property Prediction


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Deep learning and density-functional theory K Ryczko, DA Strubbe, I Tamblyn Physical Review A 100 (2), 022512, 2019	114	2019
Convolutional neural networks for atomistic systems K Ryczko, K Mills, I Luchak, C Homenick, I Tamblyn Computational Materials Science 149, 134-142, 2018	60	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	53	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	42	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	31	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	13	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	9	2020
Twin neural network regression SJ Wetzel, K Ryczko, RG Melko, I Tamblyn Applied AI Letters 3 (4), e78, 2022	5	2022
Structural characterization of water-metal interfaces K Ryczko, I Tamblyn Physical Review B 96 (6), 064104, 2017	5	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	3	2023
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
Guided Multi-objective Generative AI to Enhance Structure-based Drug Design A Kadan, K Ryczko, A Roitberg, T Yamazaki arXiv preprint arXiv:2405.11785, 2024		2024
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

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