Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry

Because collecting precise and accurate
chemistry data is often
challenging, chemistry data sets usually only span a small region
of chemical space, which limits the performance and the scope of applicability
of data-driven models. To address this issue, we integrated an active
learning machine with automatic ab initio calculations
to form a self-evolving model that can continuously adapt to new species
appointed by the users. In the present work, we demonstrate the self-evolving
concept by modeling the formation enthalpies of stable closed-shell
polycyclic species calculated at the B3LYP/6-31G­(2df,p) level of theory.
By combining a molecular graph convolutional neural network with a
dropout training strategy, the model we developed can predict density
functional theory (DFT) enthalpies for a broad range of polycyclic
species and assess the quality of each predicted value. For the species
which the current model is uncertain about, the automatic ab initio calculations provide additional training data
to improve the performance of the model. For a test set composed of
2858 cyclic and polycyclic hydrocarbons and oxygenates, the enthalpies
predicted by the model agree with the reference DFT values with a
root-mean-square error of 2.62 kcal/mol. We found that a model originally
trained on hydrocarbons and oxygenates can broaden its prediction
coverage to nitrogen-containing species via an active learning process,
suggesting that the continuous learning strategy is not only able
to improve the model accuracy but is also capable of expanding the
predictive capacity of a model to unseen species domains.