PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor Pressure Prediction

TL;DR

PUFFIN is a machine learning framework that integrates domain knowledge and transfer learning to accurately predict vapor pressure, especially when data is scarce, and offers interpretability through Antoine equation coefficients.

Contribution

The paper introduces PUFFIN, a novel neural network architecture combining inductive bias from the Antoine equation with transfer learning for improved vapor pressure prediction.

Findings

PUFFIN outperforms non-biased models in vapor pressure prediction.

The Antoine node provides interpretable Antoine coefficients.

The framework shows potential for broader physicochemical property prediction.

Abstract

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.