  Recent studies illustrate how machine learning (ML) can be used to bypass a
core challenge of molecular modeling: the tradeoff between accuracy and
computational cost. Here, we assess multiple ML approaches for predicting the
atomization energy of organic molecules. Our resulting models learn the
difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization
energies, and predict the G4MP2 atomization energy to 0.005 eV (mean absolute
error) for molecules with less than 9 heavy atoms and 0.012 eV for a small set
of molecules with between 10 and 14 heavy atoms. Our two best models, which
have different accuracy/speed tradeoffs, enable the efficient prediction of
G4MP2-level energies for large molecules and are available through a simple web
interface.
