000917554 001__ 917554
000917554 005__ 20240712112854.0
000917554 0247_ $$2doi$$a10.48550/ARXIV.2206.11776
000917554 0247_ $$2Handle$$a2128/33648
000917554 037__ $$aFZJ-2023-00756
000917554 1001_ $$0P:(DE-HGF)0$$aRittig, Jan G.$$b0
000917554 245__ $$aGraph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
000917554 260__ $$barXiv$$c2022
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000917554 520__ $$aIonic 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.
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000917554 650_7 $$2Other$$aMachine Learning (cs.LG)
000917554 650_7 $$2Other$$aChemical Physics (physics.chem-ph)
000917554 650_7 $$2Other$$aFOS: Computer and information sciences
000917554 650_7 $$2Other$$aFOS: Physical sciences
000917554 7001_ $$0P:(DE-HGF)0$$aHicham, Karim Ben$$b1
000917554 7001_ $$0P:(DE-HGF)0$$aSchweidtmann, Artur M.$$b2
000917554 7001_ $$0P:(DE-Juel1)172097$$aDahmen, Manuel$$b3$$ufzj
000917554 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b4$$eCorresponding author$$ufzj
000917554 773__ $$a10.48550/ARXIV.2206.11776
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000917554 9141_ $$y2022
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