A saddle‐shaped nanocarbon molecule was synthesized, which revealed the existence of negative Gauss curvatures on a >3‐nm molecular structure possessing 192 π‐electrons. The synthesis was facilitated by a protocol developed with Design‐of‐Experiments optimizations and machine‐learning predictions, and spectroscopy and crystallography were used to reveal the saddle‐shaped structure of the molecule. Solution‐phase analyses showed the presence of dimeric assembly, and crystallographic analyses revealed the stacked dimeric structures. The stacked crystal structure was scrutinized by various methods, including Gauss curvatures derived from the discrete surface theory of geometry, to reveal the important role of the molecular Gauss curvature in dimeric assembly.

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 3 × 3 × 3 parameter space succeeded in allocating an optimum reaction condition in the expanded 3 × 3 × 4 parameter space. The method was specifically devised for the synthesis of a macrocycle, [n]cyclo-metaphenylenes ([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.