Self-assembly of anisotropic colloids

Argun, Bahadir Rusen

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https://hdl.handle.net/2142/129850 Copy

Description

Title

Self-assembly of anisotropic colloids

Author(s)

Argun, Bahadir Rusen

Issue Date

2025-07-11

Director of Research (if dissertation) or Advisor (if thesis)

Statt, Antonia

Doctoral Committee Chair(s)

Ewoldt, Randy

Committee Member(s)

• Sing, Charles
• Peters, Baron

Department of Study

Mechanical Sci & Engineering

Discipline

Mechanical Engineering

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Molecular dynamics
• anisotropic colloids
• machine learning
• nanoplastics

Abstract

Classical particle-based simulations—such as molecular dynamics and Monte Carlo methods—are typically conducted using spherical particles. However, the particles in many systems of interest are non-spherical, either by design, as in colloidal self-assembly, or as a result of natural processes, as observed in environmental nanoplastics. While efficient overlap detection algorithms enable the simulation of anisotropic shapes, they are generally limited to hard-core repulsive interactions. An alternative is the composite bead approach, which allows for the simulation of arbitrary particle shapes with both attractive and repulsive interactions. In this method, smaller spherical beads are rigidly connected to maintain the overall geometry of a larger, complex particle. This strategy offers flexibility, as it can represent a wide range of convex and concave shapes. We use this approach to model different nanoplastic particle morphologies. To evaluate their ecological impact, it is essential to understand the fate of nanoplastics in the environment. These particles are often surrounded by natural colloids, which can promote aggregation via favorable interactions. We perform molecular dynamics and multiparticle collision dynamics simulations to understand the effect of particle shape and flow on the heteroaggregate structure and breaking behavior. We find that mostly round particles formed compact structures with a large number of neighbors, weak connection strength, and a higher fractal dimension. Microplastics with sharper edges and corners aggregated into more fractal structures with fewer neighbors, but with stronger connections. We investigated the behavior of aggregates under shear flow. The critical shear rate at which the aggregates broke up is much larger for spherical and rounded cube microplastics. For these shapes, the compact aggregate structure outweighs their weaker connection strength. The rounded cube aggregate exhibited unexpectedly high resistance to breakup under shear. We attribute this to being fairly compact due to weaker, flexible neighbor connections, which are still strong enough to prevent particles from breaking off during shear flow. Irrespective of the stronger connections between neighboring microplastics, the fractal aggregates of cubes break up at lower shear rates. We find that cube aggregates reduced their radius of gyration significantly, indicating restructuring during shear, while most neighbor connections were kept intact. Sphere aggregates, however, kept their overall size while undergoing local rearrangements, breaking a significant portion of their neighbor interactions. To accurately represent the particle shapes and obtain smooth, realistic effective pair interactions between two rigid bodies, each body may need to contain hundreds of spherical beads. Given an interacting pair of particles, traditional molecular dynamics methods calculate all inter-body distances between the beads of the rigid bodies within a certain distance. For a system containing many anisotropic particles, these distance calculations are computationally costly and limit the attainable system size and simulation time. However, the effective interaction between two rigid particles should only depend on the distance between their center of masses and their relative orientation. Therefore, a function capable of directly mapping the center of mass distance and relative orientation to the interaction energy between the two rigid bodies, would completely bypass inter-bead distance calculations. It is challenging to derive such a general function analytically for almost any non-spherical rigid body. We have employed various machine learning tools to achieve this task. The pair configuration (center of mass distance and relative orientation) is taken as input and the energy, forces and torques between two rigid particles are predicted directly. We show that molecular dynamics simulations of cubes and cylinders performed with forces and torques obtained from the gradients of the energy neural-nets quantitatively match traditional simulations that use composite rigid bodies. Both structural quantities and dynamic measures are in agreement, while achieving up to 23 times speed up over traditional molecular dynamics, depending on hardware and system size. In addition to using shape-based descriptors, we also represented pair configurations through point groups that share the same invariances as the interacting rigid bodies. This formulation enabled efficient treatment of the symmetries inherent in the pair configurations and made it possible to leverage machine learning potentials originally developed for atoms and quantum mechanical datasets. We compared machine learning potentials based on predefined versus learnable descriptors and show that, although models with learnable descriptors can achieve high predictive accuracy, their architectural complexity leads to slower inference times, limiting their practical applicability. In contrast, Neuroevolution Potential descriptors combined with fully connected neural networks strike a favorable balance between accuracy and computational efficiency. Point-based descriptors coupled with full-connected neural networks exhibit better generalization across different particle geometries and are easier to implement. Overall, our results demonstrate that machine learning can substantially accelerate molecular dynamics simulations of anisotropic particles while accurately capturing their equilibrium structures.

Graduation Semester

2025-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129850

Copyright and License Information

Copyright 2025 Bahadir Rusen Argun

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