Kyle Mills
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Deep learning and the Schrödinger equation
K Mills, M Spanner, I Tamblyn
Physical Review A 96 (4), 042113, 2017
Convolutional neural networks for atomistic systems
K Ryczko, K Mills, I Luchak, C Homenick, I Tamblyn
Computational Materials Science 149, 134-142, 2018
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
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
Crystal Site Feature Embedding Enables Exploration of Large Chemical Spaces
H Choubisa, M Askerka, K Ryczko, O Voznyy, K Mills, I Tamblyn, ...
Matter, 2020
Phase space sampling and operator confidence with generative adversarial networks
K Mills, I Tamblyn
arXiv preprint arXiv:1710.08053, 2017
Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning
C Casert, K Mills, T Vieijra, J Ryckebusch, I Tamblyn
arXiv preprint arXiv:2002.07055, 2020
Optimizing thermodynamic trajectories using evolutionary reinforcement learning
C Beeler, U Yahorau, R Coles, K Mills, S Whitelam, I Tamblyn
arXiv preprint arXiv:1903.08543, 2019
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
Finding the ground state of spin Hamiltonians with reinforcement learning
K Mills, P Ronagh, I Tamblyn
Nature Machine Intelligence 2, 509–517, 2020
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
Adversarial generation of mesoscale surfaces from small scale chemical motifs
IT Kyle Mills, Corneel Casert
Neurips 2019 (Machine Learning for Physical Sciences), 2019
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