| Deep learning and the Schrödinger equation K Mills, M Spanner, I Tamblyn Physical Review A 96 (4), 042113, 2017 | 201 | 2017 |
| 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 |
| Finding the ground state of spin Hamiltonians with reinforcement learning K Mills, P Ronagh, I Tamblyn Nature Machine Intelligence 2, 509–517, 2020 | 31 | 2020 |
| Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models K Mills, I Tamblyn Physical Review E 97 (3), 032119, 2018 | 30 | 2018 |
| Optical lattice experiments at unobserved conditions with generative adversarial deep learning C Casert, K Mills, T Vieijra, J Ryckebusch, I Tamblyn Physical Review Research 3 (3), 033267, 2021 | 11 | 2021 |
| Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning C Beeler, U Yahorau, R Coles, K Mills, S Whitelam, I Tamblyn Physical Review E 104 (6), 064128, 2021 | 10* | 2021 |
| Adversarial generation of mesoscale surfaces from small-scale chemical motifs K Mills, C Casert, I Tamblyn The Journal of Physical Chemistry C 124 (42), 23158-23163, 2020 | 8 | 2020 |
| Phase space sampling and operator confidence with generative adversarial networks K Mills, I Tamblyn arXiv preprint arXiv:1710.08053, 2017 | 6 | 2017 |
| Artificial intelligence-driven quantum computing P Ronagh, S Matsuura, KI Mills, AC Pesah US Patent App. 17/317,644, 2021 | 5 | 2021 |
| Weakly-supervised multi-class object localization using only object counts as labels K Mills, I Tamblyn arXiv preprint arXiv:2102.11743, 2021 | 2 | 2021 |
| On deep learning in physics K Mills University of Ontario Institute of Technology, 2021 | | 2021 |
| Adversarial machine learning for modeling the distribution of large-scale ultracold atom experiments C Casert, K Mills, T Vieijra, J Ryckebusch, I Tamblyn Bulletin of the American Physical Society 65, 2020 | | 2020 |
| Adversarial generation of mesoscale surfaces from small scale chemical motifs IT Kyle Mills, Corneel Casert Neurips 2019 (Machine Learning for Physical Sciences), 2019 | | 2019 |
| Discriminative and generative machine learning for spin systems based on physically interpretable features C Casert, K Mills, J Nys, J Ryckebusch, I Tamblyn, T Vieijra StatPhys 27 Main Conference, 2019 | | 2019 |
Isaac TamblynBlock & University of Ottawa & University of WaterlooVerified email at uottawa.ca
