The BO model is implemented using the open-source Python package Dragonfly, developed by Kandasamy et al. The initial runs are decided using Latin-hypercube sampling. The researchers define the input variables (parameters to be changed) and the objective to be optimized. The platform supports both single-objective and multi-objective optimization, targeting yield and/or throughput. In single-objective optimization, the model identifies the global maximum of the reaction. In multi-objective optimization, the model finds a set of non-dominated solutions known as the Pareto front. To assess the progress of the optimization problem, the platform tracks the hypervolume after each run. In cases of interrupted runs or the desire to build upon previously executed experiments, the platform allows for further optimization from that point (For further details, see Supplementary Information). The platform's integration of machine learning effectively reduces the reliance on human resources. Once the experiments are set up and the optimization process is initiated, the platform operates independently. The machine learning model autonomously determines the next set of experiments to run, and the corresponding commands are automatically transmitted to the platform. As a result, the platform can run continuously, including overnight, freeing up the chemist to focus on other tasks.