HANNA: hard-constraint neural network for consistent activity coefficient prediction.

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, achi

SUBMITTER: Specht T 

PROVIDER: S-EPMC11575590 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature