ArticleChemical science2024
HANNA: hard-constraint neural network for consistent activity coefficient prediction.
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Binary interaction parameters of a million mixtures for PCP-SAFT using deep learning.Digital discovery · 2026Article
- Prediction of Solute Descriptors for Linear Solvation Energy Relationships Using K‑Nearest Neighbors, Group Contributions, and Graph-Convolutional Neural Networks.ACS environmental Au · 2026Article
- Differentiable Thermodynamic Phase-Equilibria for Machine Learning.Journal of chemical information and modeling · 2026Article
- Physics-informed structural diagnostics of model-data agreement beyond scalar metrics.Scientific reports · 2026Article
- Thermodynamically consistent machine learning model for excess Gibbs energy.Nature communications · 2026Article
- Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods.Scientific data · 2026Article
- Physics-Embedded Machine Learning Model for Phase Equilibrium Prediction in Multicomponent Systems.Journal of chemical information and modeling · 2025Article
- Multi-fidelity graph neural networks for predicting toluene/water partition coefficients.Journal of cheminformatics · 2025Article
- Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry.Journal of chemical information and modeling · 2025Article
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Authors and funding
6 authors.
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Abstract
We present the first hard-constraint neural network model for predicting activity coefficients (HANNA), a thermodynamic mixture property that is the basis for many applications in science and engineering. Unlike traditional neural networks, which ignore physical laws and result in inconsistent predictions, our model is designed to strictly adhere to all thermodynamic consistency criteria. By leveraging deep-set neural networks, HANNA maintains symmetry under the permutation of the components. Furthermore, by hard-coding physical constraints in the model architecture, we ensure consistency with the Gibbs-Duhem equation and in modeling the pure components. The model was trained and evaluated on 317 421 data points for activity coefficients in binary mixtures from the Dortmund Data Bank, achieving significantly higher prediction accuracies than the current state-of-the-art model UNIFAC. Moreover, HANNA only requires the SMILES of the components as input, making it applicable to any binary mixture of interest. HANNA is fully open-source and available for free use.
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Registered trials
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