Designing binary mixtures of colloidal particles with simple interactions that assemble complex crystals

TL;DR

This paper presents a method to design simple pair potentials for binary colloidal mixtures that reliably self-assemble into complex crystal structures, bridging computational design and experimental realization.

Contribution

It introduces a constrained optimization approach using relative-entropy minimization to create physically feasible interactions for complex crystal assembly.

Findings

Simple interactions can assemble complex lattices like honeycomb and diamond.

Initial conditions and assembly protocols significantly influence success.

Constraints inspired by DNA-functionalized nanoparticles improve design feasibility.

Abstract

Computational methods for designing interactions between colloidal particles that induce self-assembly have received much attention for their promise to discover tailored materials. However, it often remains a challenge to translate computationally designed interactions to experiments because they may have features that are too complex, or even infeasible, to physically realize. Toward bridging this gap, we leverage relative-entropy minimization to design pair potentials for binary mixtures of colloidal particles that assemble crystal superlattices. We reduce the dimensionality and extent of the interaction design space by enforcing constraints on the form and parametrization of the pair potentials that are physically motivated by DNA-functionalized nanoparticles. We show that several two- and three-dimensional lattices, including honeycomb and cubic diamond, can be assembled using simple interactions despite their complex structures. We also find that the initial conditions used for the designed parameters as well as the assembly protocol play important roles in determining the outcome and success of the assembly process.