|Nathan Alexander Mahynski
Theory, simulation, and machine learning will be employed to understand how chemical functionalization of soft matter systems (e.g., colloids, polymers) affects their self-assembly into different crystals or other morphologies, and how this is related to their equilibrium phase behavior. Recent work has shown how phase diagrams can be predicted for multicomponent colloidal assemblies using novel structure enumeration schemes based on symmetry [1,2]. This project will focus on extending previous work and connecting it with experimentally viable design routes to enable the rational design and synthesis of colloidal crystals, monolayers, and functional materials. These symmetry considerations will especially be used to derive functionality for self-assembling nanoscale frameworks.
 “Using symmetry to elucidate the importance of stoichiometry in colloidal crystal assembly,” N. A. Mahynski, E. Pretti, V. K. Shen, J. Mittal, Nature Commun., 10 2028 (2019).
 “Symmetry-based crystal structure enumeration in two dimensions,” E. Pretti, V. K,'" E. Pretti, V. K. Shen, J. Mittal, N. A. Mahynski, J. Phys. Chem. A 124, 3276-3285 (2020).
Molecular Simulation; Computational Chemistry; Colloidal Self-assembly; Material Science; Statistical Mechanics; Inverse Design