Accurate Thermochemistry with Small Data Sets: A Bond Additivity Correction and Transfer Learning Approach

Machine learning provides promising
new methods for accurate yet
rapid prediction of molecular properties, including thermochemistry,
which is an integral component of many computer simulations, particularly
automated reaction mechanism generation. Often, very large data sets
with tens of thousands of molecules are required for training the
models, but most data sets of experimental or high-accuracy quantum
mechanical quality are much smaller. To overcome these limitations,
we calculate new high-level data sets and derive bond additivity corrections
to significantly improve enthalpies of formation. We adopt a transfer
learning technique to train neural network models that achieve good
performance even with a relatively small set of high-accuracy data.
The training data for the entropy model are carefully selected so that important conformational effects are captured.
The resulting models are generally applicable thermochemistry predictors
for organic compounds with oxygen and nitrogen heteroatoms that approach
experimental and coupled cluster accuracy while only requiring molecular
graph inputs. Due to their versatility and the ease of adding new
training data, they are poised to replace conventional estimation
methods for thermochemical parameters in reaction mechanism generation.
Since high-accuracy data are often sparse, similar transfer learning
approaches are expected to be useful for estimating many other molecular
properties.