In conclusion, this work represents an advancement in promoting the adoption of innovative digital tools by chemists in their laboratories. The integration of Bayesian optimization, experimental techniques, ab-initio simulations, and data management within ChemOS 2.0 unifies automated laboratories to conduct sophisticated workflows. Our hybrid approach not only relies on experimental results but also incorporates in-depth sample analysis through DFT calculations, enabling comprehensive data-driven research.