Stephen Whitelam

25 papers Journal 25

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Evolutionary design of thermodynamic logic gates and their heat emission. CoRR Stephen Whitelam
2025	J	`jnl`	Generative thermodynamic computing. CoRR Stephen Whitelam
2025	J	`jnl`	The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS). CoRR Andrew Ferguson, Marisa Lafleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar, E. Paulo Alves, Jeremy Avigad, Simon Billinge, Camille L. Bilodeau, Keith Brown, Emmanuel J. Candès, Arghya Chattopadhyay, Bingqing Cheng, Jonathan Clausen, Connor W. Coley, Andrew J. Connolly, Fred Daum, Sijia S. Dong, Chrisy Xiyu Du, Cora Dvorkin, Cristiano Fanelli, Eric B. Ford, Luis Manuel Frutos, Nicolás García Trillos, Cecilia Garraffo, Robert Ghrist, Rafael Gómez-Bombarelli, Gianluca Guadagni, Sreelekha Guggilam, Sergei Gukov, Juan B. Gutierrez, Salman Habib, Johannes Hachmann, Boris Hanin, Philip C. Harris, Murray Holland, Elizabeth Holm, Hsin-Yuan Huang, Shih-Chieh Hsu, Nick Jackson, Olexandr Isayev, Heng Ji, Aggelos K. Katsaggelos, Jeremy Kepner, Yannis G. Kevrekidis, Michelle P. Kuchera, J. Nathan Kutz, Branislava Lalic, Ann Lee, Matt LeBlanc, Josiah Lim, Rebecca Lindsey, Yongmin Liu, Peter Y. Lu, Sudhir Malik, Vuk Mandic, Vidya B. Manian, Emeka P. Mazi, Pankaj Mehta, Peter Melchior, Brice Ménard, Jennifer Ngadiuba, Stella Offner, Elsa Olivetti, Shyue Ping Ong, Christopher Rackauckas, Philippe Rigollet, Chad Risko, Philip Romero, Grant M. Rotskoff, Brett Savoie, Uros Seljak, David Shih, Gary Shiu, Dima Shlyakhtenko, Eva Silverstein, Taylor Sparks, Thomas Strohmer, Christopher Stubbs, Stephen Thomas, Suriyanarayanan Vaikuntanathan, René Vidal, Francisco Villaescusa-Navarro, Gregory Voth, Benjamin Wandelt, Rachel Ward, Melanie Weber, Risa Wechsler, Stephen Whitelam, Olaf Wiest, Mike Williams, Zhuoran Yang, Yaroslava G. Yingling, Bin Yu, Shuwen Yue, Ann Zabludoff, Huimin Zhao, Tong Zhang
2025	J	`jnl`	Training thermodynamic computers by gradient descent. CoRR Stephen Whitelam
2024	J	`jnl`	Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth. CoRR Leonardo Sabattini, Annalisa Coriolano, Corneel Casert, Stiven Forti, Edward S. Barnard, Fabio Beltram, Massimiliano Pontil, Stephen Whitelam, Camilla Coletti, Antonio Rossi
2024	J	`jnl`	Increasing the clock speed of a thermodynamic computer by adding noise. CoRR Stephen Whitelam
2024	J	`jnl`	Springs and a stopwatch: neural units with time-dependent multifunctionality. CoRR Stephen Whitelam
2024	J	`jnl`	Thermodynamic computing out of equilibrium. CoRR Stephen Whitelam, Corneel Casert
2023	J	`jnl`	How to train your demon to do fast information erasure without heat production. CoRR Stephen Whitelam
2022	J	`jnl`	Cellular automata can classify data by inducing trajectory phase coexistence. CoRR Stephen Whitelam, Isaac Tamblyn
2022	J	`jnl`	Demon in the machine: learning to extract work and absorb entropy from fluctuating nanosystems. CoRR Stephen Whitelam
2022	J	`jnl`	Learning stochastic dynamics and predicting emergent behavior using transformers. CoRR Corneel Casert, Isaac Tamblyn, Stephen Whitelam
2022	J	`jnl`	Training neural networks using Metropolis Monte Carlo and an adaptive variant. CoRR Stephen Whitelam, Viktor Selin, Ian Benlolo, Isaac Tamblyn
2022	J	`jnl`	Training neural networks using Metropolis Monte Carlo and an adaptive variant. Mach. Learn. Sci. Technol. Stephen Whitelam, Viktor Selin, Ian Benlolo, Corneel Casert, Isaac Tamblyn
2021	J	`jnl`	Improving the Accuracy of Nearest-Neighbor Classification Using Principled Construction and Stochastic Sampling of Training-Set Centroids. Entropy Stephen Whitelam
2021	J	`jnl`	Watch and learn - a generalized approach for transferrable learning in deep neural networks via physical principles. Mach. Learn. Sci. Technol. Kyle Sprague, Juan Carrasquilla, Stephen Whitelam, Isaac Tamblyn
2020	J	`jnl`	Correspondence between neuroevolution and gradient descent. CoRR Stephen Whitelam, Viktor Selin, Sang-Won Park, Isaac Tamblyn
2020	J	`jnl`	Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz. CoRR Corneel Casert, Tom Vieijra, Stephen Whitelam, Isaac Tamblyn
2020	J	`jnl`	Neuroevolutionary learning of particles and protocols for self-assembly. CoRR Stephen Whitelam, Isaac Tamblyn
2020	J	`jnl`	Watch and learn - a generalized approach for transferrable learning in deep neural networks via physical principles. CoRR Kyle Sprague, Juan Carrasquilla, Stephen Whitelam, Isaac Tamblyn
2019	J	`jnl`	Evolutionary reinforcement learning of dynamical large deviations. CoRR Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn
2019	J	`jnl`	Learning to grow: control of materials self-assembly using evolutionary reinforcement learning. CoRR Stephen Whitelam, Isaac Tamblyn
2019	J	`jnl`	Optimizing thermodynamic trajectories using evolutionary reinforcement learning. CoRR Chris Beeler, Uladzimir Yahorau, Rory Coles, Kyle Mills, Stephen Whitelam, Isaac Tamblyn
2018	J	`jnl`	Simple coarse graining and sampling strategies for image recognition. CoRR Stephen Whitelam
2014	J	`jnl`	Development and use of an atomistic CHARMM-based forcefield for peptoid simulation. J. Comput. Chem. Dina T. Mirijanian, Ranjan V. Mannige, Ronald N. Zuckermann, Stephen Whitelam