244 citations · 2.3k across the 68 of their papers we have counts for
7 papers · 1 filter
Neuroevolutionary learning of particles and protocols for self-assembly
Stephen Whitelam, Isaac Tamblyn
Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without i…
Varied phenomenology of models displaying dynamical large-deviation singularities
Stephen Whitelam, Daniel Jacobson
Singularities of dynamical large-deviation functions are often interpreted as the signal of a dynamical phase transition and the coexistence of distinct dynamical phases, by analog…
Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz
Corneel Casert, Tom Vieijra, Stephen Whitelam +1
We use a neural network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We obtain the scaled c…
Correspondence between neuroevolution and gradient descent
Stephen Whitelam, Viktor Selin, Sang-Won Park +1
We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient…
Hysteresis curves reveal the microscopic origin of cooperative CO adsorption in diamine-appended metal-organic frameworks
John R. Edison, Rebecca L. Siegelman, Zdenek Preisler +3
Diamine-appended metal{organic frameworks (MOFs) of the form Mg2(dobpdc)(diamine)2 adsorb CO2 in a cooperative fashion, exhibiting an abrupt change in CO2 occupancy with pressure o…
Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles
Kyle Sprague, Juan Carrasquilla, Steve Whitelam +1
Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has ena…