Abstract

Using ground-state and relative-entropy based inverse design strategies, isotropic interactions with an attractive well are determined to stabilize and promote assembly of particles into two-dimensional square, honeycomb, and kagome lattices. The design rules inferred from these results are discussed and validated in the discovery of interactions that favor assembly of the highly open truncated-square and truncated-hexagonal lattices.

Highlights

  • The manufacture of functional materials with specific nanoscale structural features presents considerable scientific and engineering challenges

  • The interaction range must span a minimum number of coordination shells. This rule of thumb states the intuitive idea that the range of an isotropic interaction potential must be large enough to distinguish the target structure compared to its competitors

  • Using ground state (GS) and RE inverse design methods, we have inferred a set of ‘design rules’ that help to understand the properties of single-well pair potentials that can stabilize three distinct two-dimensional target lattices

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Summary

Introduction

The manufacture of functional materials with specific nanoscale structural features presents considerable scientific and engineering challenges. While top-down fabrication approaches (e.g., lithography) have been advanced to address such challenges, they are often too expensive or time consuming for adoption in industrial manufacturing applications.[1,2,3] Bottom-up approaches such as self-assembly, on the other hand, stand as promising alternatives in which material building blocks (nanoparticles, block copolymers, etc.) might be designed–through modification of their effective mutual interactions4–7–to spontaneously self-organize into a state that exhibits the desired microstructural features.[8,9,10,11] To determine which interactions stabilize a desired self-assembled structure, a design strategy is needed. ‘inverse’ design strategies provide a more direct means for discovering interactions suitable for stabilizing the target structure, typically via solution of a constrained optimization problem.[12,13,14,15]

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