Eric Jankowski

Self-assembly and reconfigurability of shape-shifting particles.

Reconfigurability of two-dimensional colloidal crystal structures assembled by anisometric particles capable of changing their shape were studied by molecular dynamics computer simulation. We show that when particles change shape on cue, the assembled structures reconfigure into different ordered structures, structures with improved order, or more densely packed disordered structures, on faster time scales than can be achieved via self-assembly from an initially disordered arrangement. These results suggest that reconfigurable building blocks can be used to assemble reconfigurable materials, as well as to assemble structures not possible otherwise, and that shape shifting could be a promising mechanism to engineer assembly pathways to ordered and disordered structures.

Screening and Designing Patchy Particles for Optimized Self-Assembly Propensity Through Assembly Pathway Engineering

Self-assembly holds promise for creating new materials and devices because of its inherent parallelism, allowing many building blocks to simultaneously organize using preprogrammed interactions. An important trend in nanoparticle and colloid science is the synthesis of particles with unusual shapes and/or directional (“patchy”) interactions, whose anisotropy allows, in principle, assemblies of unprecedented complexity. However, patchy particles are more prone to long relaxation times during thermodynamically driven assembly, and there is no a priori way of predicting which particles might be good assembly candidates. Here we demonstrate a new conceptual approach to predict this information using sequences of intermediate clusters that appear during assembly. We demonstrate our approach on a family of model building blocks as well as a real system of CdTe/CdS tetrahedra and find design rules for engineering the optimized assembly of target structures.

A comparison of new methods for generating energy-minimizing configurations of patchy particles

Increasingly complex particles are pushing the limits of traditional simulation techniques used to study self-assembly. In this work, we test the use of a learning-augmented Monte Carlo method for predicting low energy configurations of patchy particles shaped like “Tetris®” pieces. We extend this method to compare it against Monte Carlo simulations with cluster moves and introduce a new algorithm—bottom-up building block assembly—for quickly generating ordered configurations of particles with a hierarchy of interaction energies.

Self Assembled Clusters of Spheres Related to Spherical Codes

We consider the thermodynamically driven self-assembly of spheres onto the surface of a central sphere. This assembly process forms self-limiting, or terminal, anisotropic clusters (N-clusters) with well-defined structures. We use Brownian dynamics to model the assembly of N-clusters varying in size from two to twelve outer spheres and free energy calculations to predict the expected cluster sizes and shapes as a function of temperature and inner particle diameter. We show that the arrangements of outer spheres at finite temperatures are related to spherical codes, an ideal mathematical sequence of points corresponding to the densest possible sphere packings. We demonstrate that temperature and the ratio of the diameters of the inner and outer spheres dictate cluster morphology. We present a surprising result for the equilibrium structure of a 5-cluster, for which the square pyramid arrangement is preferred over a more symmetric structure. We show this result using Brownian dynamics, a Monte Carlo simulation, and a free energy approximation. Our results suggest a promising way to assemble anisotropic building blocks from constituent colloidal spheres.

Calculation of Partition Functions for the Self-Assembly of Patchy Particles

Partition functions encode all the thermodynamics of a system, but for most systems of practical importance, they cannot be calculated exactly. In this work we present a new hierarchical method for calculating partition functions to arbitrary precision. We discuss the algorithmic details of our implementation, including elements of shape-matching and entropy calculation for on-lattice and off-lattice systems. We highlight computational trade-offs between speed and accuracy, showing how this varies with temperature, and demonstrate its utility in studying nanoscale self-assembly for a system of model patchy particles.

Enhanced Computational Sampling of Perylene and Perylothiophene Packing with Rigid-Body Models

Molecular simulations have the potential to advance the understanding of how the structure of organic materials can be engineered through the choice of chemical components but are limited by computational costs. The computational costs can be significantly lowered through the use of modeling approximations that capture the relevant features of a system, while lowering algorithmic complexity or by decreasing the degrees of freedom that must be integrated. Such methods include coarse-graining techniques, approximating long-range electrostatics with short-range potentials, and the use of rigid bodies to replace flexible bonded constraints between atoms. To understand whether and to what degree these techniques can be leveraged to enhance the understanding of planar organic molecules, we investigate the morphologies predicted by molecular dynamic simulations using simplified molecular models of perylene and perylothiophene. Approximately, 10 000 wall-clock hours of graphics processing unit-accelerated simulations are performed using both rigid and flexible models to test their efficiency and predictive capability with the two chemistries. We characterize the 1191 resulting morphologies using simulated X-ray diffraction and cluster analysis to distinguish structural transitions, summarized by four phase diagrams. We find that the morphologies generated by the rigid model of perylene and perylothiophene match with those generated by the flexible model. We find that ordered, hexagonally packed columnar phases are thermodynamically favored over a wide range of densities and temperatures for both molecules, in qualitative agreement with experiments. Furthermore, we find the rigid model to be more computationally efficient for both molecules, providing more samples per second and shorter times to equilibrium. Owing to the structural accuracy and improved computational efficiency of modeling polyaromatic groups as rigid bodies, we recommend this modeling choice for enhancing the sampling in polyaromatic molecular simulations.

Computational challenges in nanoparticle partition function calculation

Bottom-up building block assembly is a useful technique for determining thermodynamically stable configurations of certain physical particles. This paper provides a description of the computational bottlenecks encountered when generating large configurations of particles. We identify two components; cluster pairing and shape matching, that dominate the run time. We present scaling data for a simple example particle and discuss opportunities for enhancing implementations of bottom-up building block assembly for studying larger or more complex systems.

Self-Assembly and Self-Tuning Behavior of Self-Propelled Particles

In this work we investigate the self-organizing behavior of self-propelled, interacting particles. Using GPU-optimized molecular dynamics simulation we find steady state structures stabilized far-from-equilibrium. We show how these structures depend upon interaction parameters, thermodynamic parameters, and initial conditions.