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20242026
most citedAdaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth

24 citations · 34 across the 9 of their papers we have counts for

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Showing cond-mat.stat-mechShow all

6 papers · 1 filter

cond-mat.stat-mech2026

Work as a function of protocol duration for the efficient erasure of an underdamped memory: isothermal to adiabatic transition

Nicolas Barros, Stephen Whitelam, Sergio Ciliberto +1

We use evolutionary reinforcement learning to determine efficient time-dependent erasure protocols for an underdamped cantilever moving in a double-well potential, an experimental…

cond-mat.stat-mech2026

A neural-network Maxwell's demon learns cold damping for work extraction

Stephen Whitelam, Sergio Ciliberto, Ludovic Bellon

We train a neural-network Maxwell's demon to extract work from a model of an underdamped micromechanical cantilever subject to thermal noise. The demon, which periodically adjusts…

cond-mat.stat-mech2025

Benchmark control problems in nonequilibrium statistical mechanics

Stephen Whitelam, Corneel Casert, Megan Engel +1

We present a set of computer codes designed to test methods for optimizing time-dependent control protocols in fluctuating nonequilibrium systems. Each problem consists of a stocha…

cond-mat.stat-mech2024

Nonlinear thermodynamic computing out of equilibrium

Stephen Whitelam, Corneel Casert

We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of…

cond-mat.stat-mech2024★ 9 cited

Learning efficient erasure protocols for an underdamped memory

Nicolas Barros, Stephen Whitelam, Sergio Ciliberto +1

We apply evolutionary reinforcement learning to a simulation model in order to identify efficient time-dependent erasure protocols for a physical realization of a one-bit memory by…

cond-mat.stat-mech2024

Learning protocols for the fast and efficient control of active matter

Corneel Casert, Stephen Whitelam

We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles. By encoding the protocol in t…