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| 001 | 1025679 | ||
| 005 | 20240709082025.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2401.01874 |2 doi |
| 037 | _ | _ | |a FZJ-2024-03068 |
| 100 | 1 | _ | |a Brozos, Christoforos |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Graph Neural Networks for Surfactant Multi-Property Prediction |
| 260 | _ | _ | |c 2024 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1714560096_354 |2 PUB:(DE-HGF) |
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| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Surfactants are of high importance in different industrial sectors such as cosmetics, detergents, oil recovery and drug delivery systems. Therefore, many quantitative structure-property relationship (QSPR) models have been developed for surfactants. Each predictive model typically focuses on one surfactant class, mostly nonionics. Graph Neural Networks (GNNs) have exhibited a great predictive performance for property prediction of ionic liquids, polymers and drugs in general. Specifically for surfactants, GNNs can successfully predict critical micelle concentration (CMC), a key surfactant property associated with micellization. A key factor in the predictive ability of QSPR and GNN models is the data available for training. Based on extensive literature search, we create the largest available CMC database with 429 molecules and the first large data collection for surface excess concentration ($Γ$$_{m}$), another surfactant property associated with foaming, with 164 molecules. Then, we develop GNN models to predict the CMC and $Γ$$_{m}$ and we explore different learning approaches, i.e., single- and multi-task learning, as well as different training strategies, namely ensemble and transfer learning. We find that a multi-task GNN with ensemble learning trained on all $Γ$$_{m}$ and CMC data performs best. Finally, we test the ability of our CMC model to generalize on industrial grade pure component surfactants. The GNN yields highly accurate predictions for CMC, showing great potential for future industrial applications. |
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| 650 | _ | 7 | |a Chemical Physics (physics.chem-ph) |2 Other |
| 650 | _ | 7 | |a Machine Learning (cs.LG) |2 Other |
| 650 | _ | 7 | |a FOS: Physical sciences |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Rittig, Jan G. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Bhattacharya, Sandip |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Akanny, Elie |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Kohlmann, Christina |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Mitsos, Alexander |0 P:(DE-Juel1)172025 |b 5 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2401.01874 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1025679 |p VDB |
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