Stochastic optimal control of mesostructure of supramolecular assemblies using dissipative particle dynamics and dynamic programming with experimental validation.

The self-assembly process, where molecules form complex structures through interaction forces, has broad applications in various fields. However, controlling the dynamics of self-assembling molecules presents challenges, such as kinetic trapping, and the stochastic and nonlinear nature of governing equations. Recent studies have used Markov decision processes to control self-assemblies, but this approach may not be suitable for more complex systems and may result in disassembly or irreversible changes. To address these limitations, a novel integrated framework for the systematic development of control schemes for self-assembling systems is proposed. The framework uses a dissipative particle dynamics model to accurately capture self-assembly and generate a high-fidelity representation of relevant nanostructures. The probabilities of transitioning between states are calculated, which enables the formulation of a stochastic optimal control problem. To solve this problem, dynamic programming is employed. The framework is applied to a case study involving dynamic binary complexes with multiple unwanted metastable states, which serves to experimentally demonstrate the efficacy of the controller in driving the system towards desired morphologies. Finally, the closed-loop simulation results highlight that the proposed framework can drive the system of thermosensitive DBCs towards target morphologies while avoiding kinetic traps. [Display omitted] • A modeling and control framework is proposed for self-assembling systems. • Dissipative Particle Dynamics is used to model self-assembly at the mesoscale. • Markov state models are employed for dimensionality reduction. • A stochastic control problem is formulated and solved with dynamic programming. • The controller drives the system to the target morphology, avoiding kinetic traps. [ABSTRACT FROM AUTHOR]