Optimization of non-equilibrium self-assembly protocols using Markov state models.

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Self-assembly offers bottom-up fabrication of materials with designed architectures and functions.
• Achieving high yields of target structures is often hindered by kinetic traps, leading to long assembly times.
• Non-equilibrium assembly protocols, where system parameters change over time, are proposed to overcome kinetic limitations.

Purpose of the Study:

• To develop a computational framework for designing time-dependent protocols to maximize self-assembly yields.
• To address the challenge of kinetic accessibility in self-assembly processes.
• To optimize assembly protocols for achieving target structures, including equilibrium and metastable states.

Main Methods:

• Combined Markov state model (MSM) analysis with optimal control theory.
• Developed an adjoint-based gradient descent method for efficient protocol optimization.
• Utilized an interpolation approach to reduce the computational cost of constructing MSMs.

Main Results:

• Demonstrated significant improvements in the yields of target self-assembled structures using optimized time-dependent protocols.
• Successfully applied the framework to both a polymer folding model and a capsid assembly model.
• Showcased the ability to target equilibrium minima, long-lived metastable structures, and transient states.

Conclusions:

• The developed framework provides an efficient computational tool for designing non-equilibrium assembly protocols.
• Optimizing time-dependent parameters is crucial for overcoming kinetic traps and maximizing self-assembly yields.
• This approach holds promise for controlling the formation of complex materials with desired properties.