Leverentz, Hannah R.2013-01-232013-01-232012-09https://hdl.handle.net/11299/143256University of Minnesota Ph.D. dissertation. September 2012. Major: Chemical physics. Advisor: Donald G. Truhlar. 1 computer file (PDF); xi, 222 pages, appendices p. 178-222.Condensed-phased particles suspended in the earth's atmosphere are called aerosols. These particles have important effects on climate and human health, but the mechanisms by which many of these particles form is not well understood. Experimental techniques for studying the early stages of the formation of these particles currently are not available, so computational methods that are capable of accurately handling property calculations on tens of thousands of configurations of the molecular clusters that serve as precursors to aerosols are needed in order to make predictions about the mechanisms of aerosol formation in the atmosphere. Fragment-based computational methods are a promising avenue for the affordable and accurate calculation of properties of large molecular clusters. Fragment-based methods use linear combinations of property calculations done on fragments of the system to obtain an approximation of those properties for the entire system, rather than attempting a highly demanding computation of those properties based on considering all of the molecules in the system at once. Many successful fragment-based methods have been presented in the literature, but this thesis focuses on one fragment-based method that is particularly straightforward and easy to implement: the electrostatically embedded many-body (EE MB) method. The thesis demonstrates the ability of the EE MB method to make accurate predictions of properties of atmospherically relevant clusters and explores a few variations of the EE MB approximation that were developed specifically for Monte Carlo simulations of atmospheric nucleation.en-USChemical PhysicsThe electrostatically embedded many-body method for the efficient computation of properties of atmospherically relevant nanoparticlesThesis or Dissertation