Thermodynamics-Consistent Graph Neural Networks
This addresses the problem of accurate and thermodynamically consistent property prediction for chemical mixtures, but it is incremental as it builds on existing GNN and thermodynamics methods.
The paper tackled predicting activity coefficients for binary mixtures by proposing a thermodynamics-consistent graph neural network (GE-GNN) that uses excess Gibbs free energy and automatic differentiation, achieving high accuracy and consistency without additional loss terms.
We propose excess Gibbs free energy graph neural networks (GE-GNNs) for predicting composition-dependent activity coefficients of binary mixtures. The GE-GNN architecture ensures thermodynamic consistency by predicting the molar excess Gibbs free energy and using thermodynamic relations to obtain activity coefficients. As these are differential, automatic differentiation is applied to learn the activity coefficients in an end-to-end manner. Since the architecture is based on fundamental thermodynamics, we do not require additional loss terms to learn thermodynamic consistency. As the output is a fundamental property, we neither impose thermodynamic modeling limitations and assumptions. We demonstrate high accuracy and thermodynamic consistency of the activity coefficient predictions.