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Recent studies illustrate how machine learning (ML) can be used to bypass acore challenge of molecular modeling: the tradeoff between accuracy andcomputational cost. Here, we assess multiple ML approaches for predicting theatomization energy of organic molecules. Our resulting models learn thedifference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomizationenergies, and predict the G4MP2 atomization energy to 0.005 eV (mean absoluteerror) for molecules with less than 9 heavy atoms and 0.012 eV for a small setof molecules with between 10 and 14 heavy atoms. Our two best models, whichhave different accuracy/speed tradeoffs, enable the efficient prediction ofG4MP2-level energies for large molecules and are available through a simple webinterface.

Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations