Physical Review Letters, American Physical Society, 29 juin 2021, Volume : 127, Numéro : 1
Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote...
Journal of Computational Physics, Elsevier, 20 juillet 2017, Volume : 350
In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space...
The Journal of Physical Chemistry C, American Chemical Society, 18 novembre 2020, Volume : 124, Numéro : 48
We introduce an inverse design framework based on artificial neural networks, genetic algorithms, and tight-binding calculations, capable to optimize the very...
The Journal of Physical Chemistry C, American Chemical Society, 24 septembre 2020, Volume : 124, Numéro : 42
We demonstrate the use of a regressive upscaling generative adversarial network (RUGAN) as an effective way to sample state space for hexagonal porous graphene...
The Journal of Chemical Physics, American Institute of Physics, 27 juillet 2020, Volume : 153, Numéro : 4
We show how to bound and calculate the likelihood of dynamical large deviations using evolutionary reinforcement learning. An agent, a stochastic model,...
Canadian Journal of Physics, Canadian Science Publishing, 23 janvier 2023, Volume : 101, Numéro : 3
Using machine learning, we explore the utility of various deep neural networks when applied to high harmonic generation scenarios. First, we train the neural...
We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small...
Physical Review E, American Physical Society, 11 mai 2020, Volume : 101, Numéro : 5
We show that neural networks trained by evolutionary reinforcement learning can enact efficient molecular self-assembly protocols. Presented with molecular...
Advances in Artificial Intelligence: 33rd Canadian Conference on Artificial Intelligence, Springer Nature Switzerland AG, 6 mai 2020
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design. Recent work includes, for...
The Journal of Physical Chemistry C, American Chemical Society, 19 mai 2017, Volume : 121, Numéro : 24
Hybridized molecule/metal interfaces are ubiquitous in molecular and organic devices. The energy level alignment (ELA) of frontier molecular levels relative to...
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