A GNN-Based QSPR Model for Surfactant Properties
| dc.contributor.author | Ham, Seokgyun | en |
| dc.contributor.author | Wang, Xin | en |
| dc.contributor.author | Zhang, Hongwei | en |
| dc.contributor.author | Lattimer, Brian | en |
| dc.contributor.author | Qiao, Rui | en |
| dc.date.accessioned | 2025-01-08T14:11:56Z | en |
| dc.date.available | 2025-01-08T14:11:56Z | en |
| dc.date.issued | 2024-11-19 | en |
| dc.date.updated | 2024-12-27T14:02:05Z | en |
| dc.description.abstract | Surfactants are among the most versatile molecules in the chemical industry because they can self-assemble in bulk solutions and at interfaces. Predicting the properties of surfactant solutions, such as their critical micelle concentration (CMC), limiting surface tension (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>γ</mi></mrow><mrow><mi>c</mi><mi>m</mi><mi>c</mi></mrow></msub></mrow></semantics></math></inline-formula>), and maximal packing density (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>Γ</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula>) at water–air interfaces, is essential to their rational design. However, the relationship between surfactant structure and these properties is complex and difficult to predict theoretically. Here, we develop a graph neural network (GNN)-based quantitative structure–property relationship (QSPR) model to predict the CMC, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>γ</mi></mrow><mrow><mi>c</mi><mi>m</mi><mi>c</mi></mrow></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>Γ</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula>. Ninety-two surfactant data points, encompassing all types of surfactants—anionic, cationic, zwitterionic, and nonionic—are fed into the model, covering a temperature range of [20–30 °C], which contributes to its generalization across all surfactant types. We show that our models have high accuracy (R<sup>2</sup> = 0.87 on average in tests) in predicting the three parameters across all types of surfactants. The effectiveness of the QSPR model in capturing the variation of CMC, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>γ</mi></mrow><mrow><mi>c</mi><mi>m</mi><mi>c</mi></mrow></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>Γ</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula> with molecular design parameters are carefully assessed. The curated dataset, developed model, and critical assessment of the developed model will contribute to the development of improved surfactants QSPR models and facilitate their rational design for diverse applications. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Ham, S.; Wang, X.; Zhang, H.; Lattimer, B.; Qiao, R. A GNN-Based QSPR Model for Surfactant Properties. Colloids Interfaces 2024, 8, 63. | en |
| dc.identifier.doi | https://doi.org/10.3390/colloids8060063 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/123958 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | A GNN-Based QSPR Model for Surfactant Properties | en |
| dc.title.serial | Colloids Interfaces | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |