Surfactant Temperature-Dependent Critical Micelle Concentration Prediction with Uncertainty-Aware Graph Neural Network

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.