2026/01/08 by Jonas Düker, Lukas Hebing, Samuel Leweke +5 · 1 voice
Chemical Engineering · Engineering · Materials Science · #Catalysis and Oxidation Reactions #Machine Learning in Materials Science #Process Optimization and Integration
paper · doi:10.1021/acs.oprd.5c00384
openalex publication_date 2026/01/08 · openalex created_date 2026/01/09 · openalex updated_date 2026/07/17
We present a newly developed, data-assisted workflow at Bayer that integrates Bayesian optimization (BO) with CIME4R, an open-source data visualization tool with explainable AI features, to facilitate chemical reaction optimization. The workflow leverages the efficiency of BO for navigating high-dimensional reaction spaces while using CIME4R to visualize the algorithm’s decision-making process, exploration of the reaction space, and the influence and interactions of individual variables. These visualizations aid the interpretation of complex data sets and provide a platform for scientists to efficiently develop a deeper understanding of machine-learning-guided optimization campaigns, thereby improving accessibility and user trust, as well as decision-making efficiency. We demonstrate the workflow in a case study involving a new class of oxime amide ligands evaluated in Ullmann-type C–N cross-coupling reactions. This data-science-driven approach enabled the rapid identification of high-yielding conditions by sampling only 0.5% of the full parameter space within 4 days of experimental work. Feature-importance analysis revealed the solvent, propylene glycol methyl ether, as the most influential parameter, followed by K 3 PO 4 as the preferred base.