Abstract: Large, periodic, and condensed phase systems impose a challenge for theoretical studies due to the compromise between accuracy and computational cost in their calculations.  We present two methods that show exciting promise for treating this compromise: machine learning and quantum embedding. We exploit machine learning methods to solve this accuracy and computational cost trade-off by leveraging large data sets to train on highly accurate calculations using small molecules and then apply them to larger systems.