In this project, our objective is to evaluate active research areas in Bayesian Optimization (BO) that are applicable to thermal fluids. We aim to utilize BO to improve the process of discovering new mixtures while also developing a methodology for fine-tuning hyperparameters, taking into account constraints on thermal fluid properties. Ultimately, the final model will predict the figure of merit (FOM) against four thermophysical properties of the mixtures, enabling us to select the most efficient candidate for experimentalists and discover mixtures that reduce uncertainties in our search space. References: 1- Deshwal, A., Simon, C. M., & Doppa, J. R. (2021). Bayesian optimization of nanoporous materials. Molecular Systems Design & Engineering, 6(12), 1066-1086. 2- Jirasek, F., & Hasse, H. (2023). Combining machine learning with physical knowledge in thermodynamic modeling of fluid mixtures. Annual Review of Chemical and Biomolecular Engineering, 14, 31-51.