Isaac Tamblyn

University of Ottawa — Canada

Materials Science · Physical Sciences

26h-index2.4kcitations144works6.42yr avg
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Research Topics

Machine Learning in Materials Science(51), Advanced Chemical Physics Studies(15), Neural Networks and Applications(12), Computational Drug Discovery Methods(9), Surface Chemistry and Catalysis(9)

Publications144 total

Prediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data
Journal of Applied Physics·2025· 3 citedOpen Access
High-Entropy Alloy Electrocatalysts Screened Using Machine Learning Informed by Quantum-Inspired Similarity Analysis
ECS Meeting Abstracts·2025
Learning When to Observe: A Frugal Reinforcement Learning Framework for a High-Cost World
Communications in computer and information science·2024
High-entropy alloy electrocatalysts screened using machine learning informed by quantum-inspired similarity analysis
Matter·2024· 33 cited
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·2024· 6 citedOpen Access
Navigating Materials Space with ML-Generated Electronic Fingerprints
2024· 2 citedOpen Access
Learning stochastic dynamics and predicting emergent behavior using transformers
Nature Communications·2024· 8 citedOpen Access
Author response for "ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry"
2024
ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry
Digital Discovery·2024· 6 citedOpen Access
Machine learning is failing us
2023
Author response for "ChemGymRL: A customizable interactive framework for reinforcement learning for digital chemistry"
2023· 1 cited
Navigating Materials Space with ML-Generated Electronic Fingerprints
2023· 1 citedOpen Access
Cellular automata can classify data by inducing trajectory phase coexistence
Physical review. E·2023Open Access
Automatic graph representation algorithm for heterogeneous catalysis
APL Machine Learning·2023· 7 citedOpen Access
Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning
arXiv (Cornell University)·2023Open Access
Interpretable discovery of semiconductors with machine learning
npj Computational Materials·2023· 33 citedOpen Access
Automatic graph representation algorithm for heterogeneous catalysis
arXiv (Cornell University)·2023Open Access
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
arXiv (Cornell University)·2023· 4 citedOpen Access
fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems
arXiv (Cornell University)·2023· 2 citedOpen Access
Themed collection on Insightful Machine Learning for Physical Chemistry
Physical Chemistry Chemical Physics·2023· 2 citedOpen Access
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Frequent Co-authors

Stephen Whitelam(10), Kevin Ryczko(8), Kyle Mills(7), Jeffrey B. Neaton(7), Stanimir Bonev(6), Edward H. Sargent(4), Corneel Casert(4), Pierre Darancet(3), Sebastian J. Wetzel(3), Roger G. Melko(3), Hitarth Choubisa(3), Oleksandr Voznyy(3), Peter H. Beton(3), Juan P. Garrahan(3), Jan Vorberger(2), Burkhard Militzer(2), Michael Spanner(2), Zhiwen Chen(2), Zachary Gariepy(2), Conrard Giresse Tetsassi Feugmo(2)