Surfactant Temperature-Dependent Critical Micelle Concentration Prediction with Uncertainty-Aware Graph Neural Network
Year: 2026
Type: article
Abstract: The critical micelle concentration (CMC) is a fundamental physicochemical property of surfactants with significant implications across multiple industries. This paper presents an uncertainty-aware graph neural network (GNN) that integrates molecular structure and temperature to simultaneously predict CMC values and prediction uncertainties. Trained on a curated dataset of 2133 CMC values with temperature annotations, our GNN achieves comparatively similar performance on two external test sets from similar works. The model provides adequately calibrated uncertainty estimates that reliably quantify prediction confidence. This dual-output approach enables reliable CMC prediction with quantifiable confidence intervals, addressing a practical need for safety-critical applications where underestimation of uncertainty could have serious consequences.
Source: Chemistry
Institutions: Kazan Scientific Center, Kazan Federal University
Language: English
Subfield: Organic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7129067226