Thermodynamic stability versus kinetic accessibility: Pareto fronts for programmable self-assembly.

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Designing self-assembling building blocks faces the challenge of simultaneously achieving thermodynamic stability and kinetic accessibility.
• These two objectives are often in competition, with limited understanding of how to optimize them concurrently.

Purpose of the Study:

• To investigate the tradeoff between equilibrium probability and folding rate in self-assembling systems using multi-objective optimization.
• To identify design principles for self-assembling building blocks that minimize the thermodynamic-kinetic tradeoff.

Main Methods:

• Development of a genetic algorithm to compute Pareto fronts for a model system of colloidal polymers.
• Utilizing a coarse-grained model for particle dynamics to efficiently search parameter space.
• Comparison of coarse-grained model predictions with Brownian dynamics simulations.

Main Results:

• A tradeoff between folding rate and equilibrium probability exists for small numbers of particle types, with bond strength influencing the outcome.
• The tradeoff disappears when the number of particle types reaches a critical value (m*).
• Short-ranged isotropic interactions generally lead to a tradeoff, necessitating orientation-dependent interactions for larger systems.

Conclusions:

• Multi-objective optimization via Pareto fronts is a viable approach to identify optimal designs for self-assembling systems.
• Avoiding the thermodynamic-kinetic tradeoff in self-assembly typically requires a specific number of particle types and potentially orientation-dependent interactions.