@article{THESIS,
      recid = {4762},
      author = {Ma, Yutao},
      title = {Data-Driven Design of Self-Assembling Soft Materials},
      publisher = {University of Chicago},
      school = {Ph.D.},
      address = {2022-08},
      number = {THESIS},
      pages = {145},
      abstract = {Self-assembly refers to a process in which initially  disordered systems spontaneously form ordered structures  over time driven by the interactions between the building  blocks. The self-assembly of soft materials systems, such  as colloids and peptides, has drawn a lot of research  interest. When their components and intermolecular  interactions are carefully calibrated, these systems could  self-assemble into interesting nanostructures with  practical applications, such as colloidal crystalline  lattices or peptide nanorods. A major area of study in  self-assembly is then the rational design of the building  blocks and interactions between them to direct these  systems to self-assemble into target nanostructures. This  is the so-called "inverse design" problem, where we are  given a target nanostructure and would like to figure out  the optimal building block and interactions that could lead  to the target structure. In our works, we explore and  develop various inverse design techniques for anisotropic  colloids and peptides. We begin by employing molecular  simulation techniques, such as molecular dynamics and Monte  Carlo simulations, to characterize the ability of a  particular building block design to form the target  structure. Then we use modern optimization and machine  learning algorithms to find the optimal design that could  lead to maximum ability to form the target structure. Based  on this general methodology, we have developed (1) inverse  design protocols that optimize anisotropic colloids to  self-assemble into target crystalline lattices with  omnidirectional optical bandgaps by combining molecular  simulation with stochastic optimization algorithm and (2)  high-throughput screening pipeline to find optimal peptides  that could self-assemble into vesicular structures as  chassis materials for synthetic cells using molecular  simulation and Bayesian optimization.},
      url = {http://knowledge.uchicago.edu/record/4762},
      doi = {https://doi.org/10.6082/uchicago.4762},
}