Tier-grown Expansion of Design-of-Experiments Parameter Spaces for Synthesis of a Nanometer-scale Macrocycle

A method to find optimum synthetic conditions was devised by combining a data-driven empirical model with a traditional mechanistic model. In this method, an experimental parameter space was empirically obtained by Design-of-Experiments optimizations with machine-learning supplements and was strategically expanded by examination of the mechanistic model of the reaction paths. An extra tier grown on the original 3x3x3 parameter space succeeded in allocating an optimum reaction condition in the expanded 3x3x4 parameter space. The method was specifically devised for the synthesis of a macrocycle, [n]cyclo-meta-phenylenes ([n]CMP), and the largest congener with n=12 was synthesized and fully characterized for the first time. Crystallographic and photophysical analyses revealed favorable features of [12]CMP for the material applications.

Automated summary

A method combining a data-driven empirical model with a traditional mechanistic model was developed to find the optimum synthetic conditions. The synthesis of a macrocycle with favorable features was achieved using this method.