Predicting Thermodynamic Properties over Combinatorially Large Chemical Spaces

Abstract

Current computational property prediction methods are limited in the number of molecules they can test at once. To predict properties for thousands or millions of molecules at once, new techniques must be developed with efficient computational scaling in the number of molecules simultaneously tested. In this dissertation, I develop a general approach to carry out computational alchemical free energy calculations using a variance minimized linear basis function approach. This approach provides a means to collect data for statistical free energy estimates that scales efficiently with the number of thermodynamic states or tested molecules. I achieve efficient speed-up over relying on simulation force code to compute energies required for free energy estimation.