Federated learning lets chemical companies train shared models on private data, and two case studies show it approaches centralized accuracy while outperforming isolated training.
Thermodynamics-Consistent Graph Neural Networks
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abstract
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.
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Federated Learning from Molecules to Processes: A Perspective
Federated learning lets chemical companies train shared models on private data, and two case studies show it approaches centralized accuracy while outperforming isolated training.