Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids

Computers & Chemical Engineering, vol. 171, pp. 108153

Abstract

Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MCMs), transformers, and graph neural networks (GNNs) have shown high accuracy in predicting ACs of binary mixtures , superior to well-established models, e.g., COSMO-RS and UNIFAC. GNNs are particularly promising here as they learn a molecular graph-to-property relationship without pretraining, typically required for transformers, and are, unlike MCMs, applicable to molecules not included in training. For ILs, however, GNN applications are currently missing. Herein, we present a GNN to predict temperature-dependent infinite dilution ACs of solutes in ILs. We train the GNN on a database including more than 40,000 AC values and compare it to a state-of-the-art MCM. The GNN and MCM achieve similar high prediction performance, with the GNN additionally enabling high-quality predictions for ACs of solutions that contain ILs and solutes not considered during training.

Authors 5

1. Jan G. Rittig Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Forckenbeckstraße 51, 52074 Aachen, Germany

2. Karim Ben Hicham Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Forckenbeckstraße 51, 52074 Aachen, Germany

3. Artur M. Schweidtmann

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Department of Chemical Engineering, Van der Maasweg 9, Delft 2629 HZ, The Netherlands

4. Manuel Dahmen

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research IEK-10 – Energy Systems Engineering, Wilhelm-Johnen-Str., 52425 Jülich, Germany

5. Alexander Mitsos corresponding Aachen Process Systems Engineering (AVT.SVT)

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research IEK-10 – Energy Systems Engineering, Wilhelm-Johnen-Str., 52425 Jülich, Germany

   JARA Center for Simulation and Data Science (CSD), Aachen, Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Forckenbeckstraße 51, 52074 Aachen, Germany