Lastly, we deployed and executed a closed-loop workflow for the synthesis of the experimental loop, the computational job is sent to the AiiDA daemon to obtain the simulated spectral gain factor. (f) The results from the two previous steps are used to re-train the Bayesian optimizer and query another candidate. All logging information was stored in the databases using the database architecture described in Subsection 2.2. The experimental gain cross section value was estimated experimentally using fluorescence spectra, relative quantum yields, and emission rates of the BSBCz derivatives using our automated optical characterization platform. Details of the calculation can be found in previous work. In summary, ChemOS 2.0 was able to successfully orchestrate a full SDL loop, including manipulation of different instruments, an AI experiment planner, and stored all output data in an internal database.