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.

The critical micelle concentration (CMC) is a fundamental physicochemical property of surfactants with significant implications across multiple industries. This paper presents uncertainty-aware graph neural network that integrates molecular structure and temperature to simultaneously predict CMC values and prediction uncertainties. Trained on a curated dataset of 1,829 surfactants with temperature annotations, our GNN achieves competitive performance (RMSE = 0.352, MAE = 0.244) on an external test set, outperforming previous models in RMSE. The model provides statistically sound, 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.

This paper describes a hardware and software platform that enables automated chemical syntheses, including the preparation, heating, and mixing of reaction mixtures, as well as post-synthesis dilution sampling and sending for high-performance liquid chromatography (HPLC) analysis, followed by automated processing of the results. A custom Python library, ChemBot, was developed to control individual robotic devices, and a client web server was created to manage the entire system. A web interface was created to view the system status and the progress of syntheses. The performance of the entire platform for performing experiments was tested by performing aldol condensation syntheses, where the ratio of reagents, the catalyst and its amount, the temperature and time of synthesis were varied. Writing custom code to monitor and control the entire system is an important step toward integrating the robotic system with artificial intelligence (AI), which will ultimately enable the transition to an autonomous laboratory, where target molecule prediction and synthesis, experimental execution and analysis, and, if necessary, refinement or modification of the model will be performed automatically, without the need for human intervention.