Development and use of an atomistic CHARMM-based forcefield for peptoid simulation. - Dina T. Mirijanian; Ranjan V. Mannige; Ronald N. Zuckermann; Stephen Whitelam (J. Comput. Chem., 2014)

Simple coarse graining and sampling strategies for image recognition. - Stephen Whitelam (CoRR, 2018)

Optimizing thermodynamic trajectories using evolutionary reinforcement learning. - Chris Beeler; Uladzimir Yahorau; Rory Coles; Kyle Mills; Stephen Whitelam; Isaac Tamblyn (CoRR, 2019)

Evolutionary reinforcement learning of dynamical large deviations. - Stephen Whitelam; Daniel Jacobson; Isaac Tamblyn (CoRR, 2019)

Learning to grow: control of materials self-assembly using evolutionary reinforcement learning. - Stephen Whitelam; Isaac Tamblyn (CoRR, 2019)

Correspondence between neuroevolution and gradient descent. - Stephen Whitelam; Viktor Selin; Sang-Won Park; Isaac Tamblyn (CoRR, 2020)

Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz. - Corneel Casert; Tom Vieijra; Stephen Whitelam; Isaac Tamblyn (CoRR, 2020)

Neuroevolutionary learning of particles and protocols for self-assembly. - Stephen Whitelam; Isaac Tamblyn (CoRR, 2020)

Improving the Accuracy of Nearest-Neighbor Classification Using Principled Construction and Stochastic Sampling of Training-Set Centroids. - Stephen Whitelam (Entropy, 2021)

Watch and learn - a generalized approach for transferrable learning in deep neural networks via physical principles. - Kyle Sprague; Juan Carrasquilla; Stephen Whitelam; Isaac Tamblyn (Mach. Learn. Sci. Technol., 2021)

Learning stochastic dynamics and predicting emergent behavior using transformers. - Corneel Casert; Isaac Tamblyn; Stephen Whitelam (CoRR, 2022)

Cellular automata can classify data by inducing trajectory phase coexistence. - Stephen Whitelam; Isaac Tamblyn (CoRR, 2022)

Training neural networks using Metropolis Monte Carlo and an adaptive variant. - Stephen Whitelam; Viktor Selin; Ian Benlolo; Isaac Tamblyn (CoRR, 2022)

Demon in the machine: learning to extract work and absorb entropy from fluctuating nanosystems. - Stephen Whitelam (CoRR, 2022)

Training neural networks using Metropolis Monte Carlo and an adaptive variant. - Stephen Whitelam; Viktor Selin; Ian Benlolo; Corneel Casert; Isaac Tamblyn (Mach. Learn. Sci. Technol., 2022)

How to train your demon to do fast information erasure without heat production. - Stephen Whitelam (CoRR, 2023)

Springs and a stopwatch: neural units with time-dependent multifunctionality. - Stephen Whitelam (CoRR, 2024)

Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth. - Leonardo Sabattini; Annalisa Coriolano; Corneel Casert; Stiven Forti; Edward S. Barnard; Fabio Beltram; Massimiliano Pontil; Stephen Whitelam; Camilla Coletti; Antonio Rossi (CoRR, 2024)

Increasing the clock speed of a thermodynamic computer by adding noise. - Stephen Whitelam (CoRR, 2024)

Thermodynamic computing out of equilibrium. - Stephen Whitelam; Corneel Casert (CoRR, 2024)

Generative thermodynamic computing. - Stephen Whitelam (CoRR, 2025)