Nonequilibrium Self-Assembly Control by the Stochastic Landscape Method.

Self-assembly of building blocks is a fundamental process in nanotechnology, materials science, and biological systems, offering pathways to the formation of complex and functional structures through local interactions. However, the lack of effective error correction mechanisms often limits the efficiency and precision of assembly, particularly in systems with strong binding energies. Inspired by cellular processes and stochastic resetting, we present a closed-loop feedback control method that employs transient modulations in interaction energies, mimicking, for instance, the global effect of pH changes as nonequilibrium drives to optimize assembly outcomes in real time. By leveraging the stochastic landscape method, a framework using energy trend-based segmentation to predict self-assembly behavior, our approach dynamically analyzes the system's state and energy trends to guide control actions. We show that the transient energy modulation during kinetic trapping conditions substantially enhances assembly yields and reduces assembly times across diverse scenarios. This strategy provides a broadly applicable, data-driven framework for optimizing nonequilibrium assembly processes, with potential implications for precision manufacturing and responsive materials design, while also advancing our understanding of controlled molecular assembly in biological and synthetic contexts.