Inverse Design for Self Assembly via On-the-Fly Optimization

TL;DR

This paper presents a novel, simulation-based inverse design method that uses iterative optimization and machine learning to create pair potentials for targeted self-assembled lattices, improving upon traditional energy comparison approaches.

Contribution

It introduces a probabilistic, on-the-fly optimization strategy that refines pair potentials based on actual assembly processes, enabling more effective design of self-assembling materials.

Findings

Successfully designed potentials for multiple lattice types

Demonstrated improved assembly from fluid to crystal phases

Applied machine learning to optimize self-assembly pathways

Abstract

Inverse methods of statistical mechanics have facilitated the discovery of pair potentials that stabilize a wide variety of targeted lattices at zero temperature. However, such methods are complicated by the need to compare, within the optimization framework, the energy of the desired lattice to all possibly relevant competing structures, which are not generally known in advance. Furthermore, ground-state stability does not guarantee that the target will readily assemble from the fluid upon cooling from higher temperature. Here, we introduce a molecular dynamics simulation-based, optimization design strategy that iteratively and systematically refines the pair interaction according to the fluid and crystalline structural ensembles encountered during the assembly process. We successfully apply this probabilistic, machine-learning approach to the design of repulsive, isotropic pair potentials that assemble into honeycomb, kagome, square, rectangular, truncated square and truncated hexagonal lattices.