ExPUFFIN: Thermodynamic Consistent Viscosity Prediction in an Extended Path-Unifying Feed-Forward Interfaced Network

TL;DR

ExPUFFIN is a hybrid GNN model that predicts liquid viscosity of hydrocarbons with thermodynamic consistency, improving accuracy and robustness over purely data-driven models by embedding physics-based inductive biases.

Contribution

This work introduces ExPUFFIN, a novel hybrid GNN framework that enforces thermodynamic consistency in viscosity predictions, addressing limitations of existing models.

Findings

Reduces RMSE by 37% compared to baseline

Provides smooth, physically consistent viscosity-temperature curves

Enhances transferability and robustness of predictions

Abstract

Accurate prediction of liquid viscosity is essential for process design and simulation, yet remains challenging for novel molecules. Conventional group-contribution models struggle with isomer discrimination, large molecules, and parameter availability, while purely data-driven graph neural networks (GNNs) demand large datasets and offer limited interpretability. Even when feasible to be applied, purely data-driven models lack thermodynamic consistency in their predictions and are not a reliable solution. This work introduces ExPUFFIN, an extended version of the Path-unifying Feed-Forward Interfaced Network, consisting of a hybrid GNN-based framework that directly predicts temperature-dependent viscosities of pure hydrocarbons from molecular graphs, while enforcing mechanistic inductive biases in the output layer to ensure thermodynamic consistency. Molecular information is given as graph structures, encoded as a graph convolutional network, and mapped to an inductive bias neuron based on two thermophysical correlations: a three-parameter Andrade-type equation and a four-parameter empirical viscosity-temperature relation. The accuracy of these models is compared with a solely data-driven prediction. The Andrade-based ExPUFFIN variant reduces RMSE compared to the purely data-driven baseline of 37 percent and yields smooth, physically consistent interpolation and extrapolation of viscosity-temperature curves, properties that are not observed in purely data-driven models. The empirical ExPUFFIN model provides comparable accuracy while retaining robust trends. Overall, embedding physics-based structure in GNN outputs improves accuracy, robustness, and transferability, enabling reliable viscosity predictions for complex hydrocarbon molecules. The approach is readily extendable to other properties and significantly broader chemical domains.