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

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.

7 pages, 5 figures

References in corpus (4)

• Perspective: Inverse methods for material design
• Inverse design of simple pairwise interactions with low-coordinated 3D lattice ground states
• Inverse Statistical Mechanics: Probing the Limitations of Isotropic Pair Potentials to Produce Ground-State Structural Extremes
• Dimensionality and design of isotropic interactions that stabilize honeycomb, square, simple cubic, and diamond lattices

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• Two-step nucleation in a binary mixture of Patchy Particles
• Metadensity functional learning for classical fluids: Regularizing with pair correlations
• Designing binary mixtures of colloidal particles with simple interactions that assemble complex crystals
• relentless: Transparent, reproducible molecular dynamics simulations for optimization
• Free-Energy Functional Approach to Inverse Problems for Self-Assembly of Three-Dimensional Crystals