UC San Diego

The goal of predictive assembly is to use tunable components that can self-assemble into targeted structures. Components include, but are not limited to, solvent characteristics, ligand-particle parameters, and protocol design. Using computational methods, including DFT calculations, molecular dynamics, and machine learning, self-assembly components can be designed and in turn, structure can be predicted. The first chapter will address the solvent-aspect of self-assembly, where the structuring of a liquid can play a key role in self-assembling processes, such as protein folding and hydrophobic aggregation. Liquid structures can be quantified by the excess entropy, which is typically estimated using the 2-body entropy. We show that this method of estimating the excess entropy is insufficient in capturing the full structuring of liquid water, a common solvent, and using a modified form of the 2-Phase Thermodynamics Method, we can efficiently and accurately calculate the excess entropy of liquid water. In the second chapter, we use DFT calculations to predict binding energies of isocyanide ligands with different degrees of steric encumbrance. These DFT calculations are used to parameterize ligand-nanoparticle interactions and create a force field for MD simulations. These simulations capture the dynamics of ligands binding to nanoparticle surfaces. From these MD trajectories, we show how simulated Raman spectroscopy can grant insights into how well a ligand is bound to different sites on a nanoparticle. The last chapter addresses self-assembly at the mesoscale, where neuroevolutionary methods, like the L2G framework can be used to predict self-assembly protocols of tiling structures. This framework was restructured and adapted to predict protocols of exotic n-vertex tiling structures, which have been challenging to create experimentally. Additional fitness functions, such as a unitcell-based scoring metric, lead to improved protocol search for complex tilings. Lastly, we show how a pressure parameter was implemented to enable the L2G framework to better align with experimental parameters. Altogether, this body of work addresses aspects of self-assembly from the atomistic to mesoscale range.