The ability to programmatically assemble colloidal micro/nanostructures into highly ordered superstructures is of great importance in both fundamental studies and practical applications. In addition to the sophisticated manipulation of the short‐range and long‐range interactions imposed on the colloidal building blocks, the intrinsic shape elements including face, edge, corner, concave, convex, and curvature also play very important roles in solving the “jigsaw puzzle” of the superstructures. Here, the recent progress in the development of colloidal assembly strategies for the formation of complex superstructures is reviewed, with a primary focus on the unique effects of the morphology of the building blocks to the assembly processes and the final structures. Overall, this Review aims to shed light on the fundamental understanding of the colloidal behaviors of complex micro/nanostructures and promote the continued development of effective strategies for the creation of functional materials with complex compositions and morphologies.
The self-assembly of complex structures from a set of non-identical building blocks is a hallmark of soft matter and biological systems, including protein complexes, colloidal clusters, and DNA-based assemblies. Predicting the dependence of the equilibrium assembly yield on the concentrations and interaction energies of building blocks is highly challenging, owing to the difficulty of computing the entropic contributions to the free energy of the many structures that compete with the ground state configuration. While these calculations yield well known results for spherically symmetric building blocks, they do not hold when the building blocks have internal rotational degrees of freedom. Here we present an approach for solving this problem that works with arbitrary building blocks, including proteins with known structure and complex colloidal building blocks. Our algorithm combines classical statistical mechanics with recently developed computational tools for automatic differentiation. Automatic differentiation allows efficient evaluation of equilibrium averages over configurations that would otherwise be intractable. We demonstrate the validity of our framework by comparison to molecular dynamics simulations of simple examples, and apply it to calculate the yield curves for known protein complexes and for the assembly of colloidal shells.
more » « less- NSF-PAR ID:
- 10479724
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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