Fully Automated Reaction Operation Driven by Accurate Inline FTIR Analysis based on Linear-Combination Strategy

To accelerate advancements in organic chemistry research and development, we propose a fully automated system based on real-time inline analysis performed by Fourier-transform infrared spectroscopy and assisted by a neural network model. To rapidly collect data, a linear combination of spectral intensities was used as training data for a yield prediction model. Using this model, we demonstrated real-time yield prediction of Suzuki-Miyaura cross-coupling with remarkable accuracy. By combining this yield prediction model with real-time inline analysis and a flow chemistry setup, we have developed a fully automated system for the rapid and efficient optimization of reaction conditions and process analysis.