Sun, Jimmy2014-08-082014-08-082014Sun, Hong Yang. 2014. Learning over Molecules: Representations and Kernels. Bachelor's thesis, Harvard College.http://nrs.harvard.edu/urn-3:HUL.InstRepos:12705171In this paper, we tackle machine learning over molecular space by considering three representations for molecules: (1) a vector of molecular properties that we treat as predictor variables, (2) a graph that captures the relationship between individual atoms in a molecule, and (3) a cheminformatic fingerprint that “identifies” a molecule. We assess the viability of each representation by training a model to predict energy values. In particular, we look a class of models that use kernel methods, whereby the prediction algorithm relies on a similarity measure between training data. On a subset of the Harvard Clean Energy Project (CEP) database, we find a simple fingerprint similarity kernel to be the fastest and most accurate for predicting HOMO-LUMO energy gap values.en-USThesis or Dissertation2014-08-08