@article{Coarse-grained:1681,
      recid = {1681},
      author = {Dannenhoffer-Lafage, Thomas},
      title = {Basis Sets and Optimization for Coarse-grained Models},
      publisher = {University of Chicago},
      school = {Ph.D.},
      address = {2018-06},
      pages = {131},
      abstract = {Coarse-Grained (CG) models provide a promising direction  to study variety of chemical systems at a reduced  computational cost. CG model are generated by reducing the  representation of a molecular system from atoms to beads.  However, how these models are parameterized can greatly  affect the reliability and the insight that could be  provided by CG models. In my thesis, work is presented on  different parameterization schemes and basis sets that can  be utilized to produce CG models. First, the affect of  parameterizing models with the Experiment Directed  Simulation (EDS) methodology is explored theoretically and  practically. This provides a foundation for top-down  information to be incorporated systematically into CG  models via EDS. Second, an implementation of the EDS  methodology that uses CG variables as targets is presented,  called Coarse Grain Directed Simulation. This allows for  small part of a much larger system to be modeled in the  effective environment of the larger system while only  minimally biasing the simulated part of the simulation.  Thirdly, a reactive methodology call reactive Multiscale  Coarse-Graining is discussed. This takes advantage of a  matrix style Hamiltonian that allows for multiple states of  a system to be represented, allowing for features such as  bond breaking and forming within a coarse-grained  simulation based on the free energy of the system. Also, a  comparison of Multiscale Coarse-graind (MS-CG) and Relative  Entropy Minimization (REM) parameterization methodologies  is explored for the case of a CG lipid bilayer within an  implicit solvent. This comparison explores the ability for  MS-CG and REM to model solvent-solute interaction when the  solvent particles have been integrated away, removing the  vector of interaction between the solvent and solute  particles. Lastly, global basis sets for both REM and MS-CG  are presented. Global basis sets provide a path that  eliminates the issue of poorly sampled basis sets that are  characterized by rare events and gives solution that have  correct boundary conditions by design. Taken together, this  work provides the foundation for understanding how  different types of information can be taken into account in  CG models via these different parameterization schemes and  basis sets.},
      url = {http://knowledge.uchicago.edu/record/1681},
      doi = {https://doi.org/10.6082/uchicago.1681},
}