SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients

Abstract

The availability of property data is one of the major bottlenecks in the development of chemical processes, often requiring time-consuming and expensive experiments or limiting the design space to a small number of known molecules. This bottleneck has been the motivation behind the continuing development of predictive property models. For the property prediction of novel molecules, group contribution methods have been groundbreaking. In recent times, machine learning has joined the more established property prediction models. However, even with recent successes, the integration of physical constraints into machine learning models remains challenging. Physical constraints are vital to many thermodynamic properties, such as the Gibbs-Duhem relation, introducing an additional layer of complexity into the prediction. Here, we introduce SPT-NRTL, a machine learning model to predict thermodynamically consistent activity coefficients and provide NRTL parameters for easy use in process simulations. The results show that SPT-NRTL achieves higher accuracy than UNIFAC in the prediction of activity coefficients across all functional groups and is able to predict many vapor-liquid-equilibria with near experimental accuracy, as illustrated for the exemplary mixtures water/ethanol and chloroform/n-hexane. To ease the application of SPT-NRTL, NRTL-parameters of 100 000 000 mixtures are calculated with SPT-NRTL and provided online.

NRTL parameters for 100 000 000 are currently hosted here: https://polybox.ethz.ch/index.php/s/unM7rbgj2FQPFdy

References in corpus (6)

• Transfer learning for solvation free energies: from quantum chemistry to experiments
• SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery
• Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
• Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
• A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing
• Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention

Cited by in corpus (5)

• Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction
• HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction
• Thermodynamics-Consistent Graph Neural Networks
• GraphXForm: Graph transformer for computer-aided molecular design
• Prediction of Activity Coefficients by Similarity-Based Imputation using Quantum-Chemical Descriptors