Molecular Simulation and Design of High-χ Low-N Block Oligomers for Control of Self-Assembly

Abstract

Multi-component oligomer systems are exciting candidates for nanostructured functional materials, due to the wide variety of their self-assembled morphologies with extremely small feature size. However, experimentally screening through the vast design space of molecular architectures can be extremely laborious. Therefore, guidance from predictive modeling is essential to reduce the synthetic effort. This dissertation discusses the predictive design of self-assembling block oligomer systems using molecular simulations, and the development of computer vision models for automated morphology detection for simulation trajectories. Work presented in this thesis creates a roadmap for efficient computational screening of shape-filling molecules, thus accelerating the design and discovery of nanostructured functional materials. First, with the aid of experimentally-validated force fields, molecular dynamics simulations were exploited to design: 1) a series of symmetric triblock oligomers that can self-assemble into ordered nanostructures with sub-1 nm domains and full domain pitches as small as 1.2 nm, 2) Blends of a lamellar-forming diblock oligomer and a cylinder-forming miktoarm star triblock oligomer leading to stable gyroid networks over a large composition window. Similarities and distinctions between the self-assembly phase behavior of these block oligomers and block polymers are discussed. Second, existing simulation data were used to train deep learning models based on three-dimensional point clouds and voxel grids. The pretrained neural networks can readily detect equilibrium morphologies, and also give rich insights of emerging patterns throughout new simulations with different system sizes and molecular dimensions.