  Accurately predicting vapor pressure is vital for various industrial and
environmental applications. However, obtaining accurate measurements for all
compounds of interest is not possible due to the resource and labor intensity
of experiments. The demand for resources and labor further multiplies when a
temperature-dependent relationship for predicting vapor pressure is desired. In
this paper, we propose PUFFIN (Path-Unifying Feed-Forward Interfaced Network),
a machine learning framework that combines transfer learning with a new
inductive bias node inspired by domain knowledge (the Antoine equation) to
improve vapor pressure prediction. By leveraging inductive bias and transfer
learning using graph embeddings, PUFFIN outperforms alternative strategies that
do not use inductive bias or that use generic descriptors of compounds. The
framework's incorporation of domain-specific knowledge to overcome the
limitation of poor data availability shows its potential for broader
applications in chemical compound analysis, including the prediction of other
physicochemical properties. Importantly, our proposed machine learning
framework is partially interpretable, because the inductive Antoine node yields
network-derived Antoine equation coefficients. It would then be possible to
directly incorporate the obtained analytical expression in process design
software for better prediction and control of processes occurring in industry
and the environment.
