This profile is built from public research funding records (CIHR, NSERC and SSHRC) and PubMed. We have not imported them from a University of Ottawa directory, so their courses may be missing. Find their university profile.
Latest papers
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 : an Institute of Physics journal · 2024
Learning stochastic dynamics and predicting emergent behavior using transformers.
Nature communications · 2024
Themed collection on Insightful Machine Learning for Physical Chemistry.
Physical chemistry chemical physics : PCCP · 2023
Latest funding
- $60,000
Artificial Intelligence Improvement of Rate Theory Models
NSERC · 2021 · Co-investigator
- $204,000
Deep learning and computational nanoscience
NSERC · 2018 · Principal investigator
17 publications.
Efficient determination of Born-effective charges, LO-TO splitting, and Raman tensors of solids with a real-space atom-centered deep learning approach.
Malenfant-Thuot O, Ryczko K, Tamblyn I, Côté M
Learning stochastic dynamics and predicting emergent behavior using transformers.
Casert C, Tamblyn I, Whitelam S
Themed collection on Insightful Machine Learning for Physical Chemistry.
Clark AE, Dral PO, Tamblyn I, Isayev O
Cellular automata can classify data by inducing trajectory phase coexistence.
Whitelam S, Tamblyn I
Machine Learning Diffusion Monte Carlo Energies.
Ryczko K, Krogel JT, Tamblyn I
High-Throughput Evaluation of Emission and Structure in Reduced-Dimensional Perovskites.
Anwar H, Johnston A, Mahesh S, Singh K, Wang Z, Kuntz DA, Tamblyn I, Voznyy O, Privé GG, Sargent EH
Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning.
Ryczko K, Wetzel SJ, Melko RG, Tamblyn I
Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning.
Beeler C, Yahorau U, Coles R, Mills K, Whitelam S, Tamblyn I
Golem: an algorithm for robust experiment and process optimization.
Aldeghi M, Häse F, Hickman RJ, Tamblyn I, Aspuru-Guzik A
Correspondence between neuroevolution and gradient descent.
Whitelam S, Selin V, Park SW, Tamblyn I
Artificial Intelligence Improvement of Rate Theory Models
Principal investigators: Daymond, Mark MR
Keywords: diffusion of defects; materials science; neutral networks; nuclear materials; rate theory
Deep learning and computational nanoscience
Principal investigators: Tamblyn, Isaac
From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.
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