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Physical Review. E|June 25, 2020
Learning to grow: Control of material self-assembly using evolutionary reinforcement learningStephen Whitelam, Isaac TamblynPhysical Review Letters|July 16, 2021
Neuroevolutionary Learning of Particles and Protocols for Self-AssemblyStephen Whitelam, Isaac TamblynPhysical Review. E|August 16, 2023
Cellular automata can classify data by inducing trajectory phase coexistenceStephen Whitelam, Isaac TamblynThe Journal of Chemical Physics|August 6, 2020
Evolutionary reinforcement learning of dynamical large deviationsStephen Whitelam, Daniel Jacobson, Isaac TamblynNature Communications|February 29, 2024
Learning stochastic dynamics and predicting emergent behavior using transformersCorneel Casert, Isaac Tamblyn, Stephen WhitelamPhysical Review Letters|October 1, 2021
Dynamical Large Deviations of Two-Dimensional Kinetically Constrained Models Using a Neural-Network State AnsatzCorneel Casert, Tom Vieijra, Stephen Whitelam, et al.Nature Communications|November 3, 2021
Correspondence between neuroevolution and gradient descentStephen Whitelam, Viktor Selin, Sang-Won Park, et al.Physical Review Letters|March 10, 2012
Random and ordered phases of off-lattice rhombus tilesStephen Whitelam, Isaac Tamblyn, Peter H Beton, et al.Physical Review Letters|April 4, 2015
Emergent rhombus tilings from molecular interactions with M-fold rotational symmetryStephen Whitelam, Isaac Tamblyn, Juan P Garrahan, et al.Physical Review. E|January 15, 2022
Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learningChris Beeler, Uladzimir Yahorau, Rory Coles, et al.Pageof 12