Machine‐Learning‐Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons. (24th September 2021) - Wakiya, Takuma; Kamakura, Yoshinobu; Shibahara, Hiroki; Ogasawara, Kazuyoshi; Saeki, Akinori; Nishikubo, Ryosuke; Inokuchi, Akihiro; Yoshikawa, Hirofumi; Tanaka, Daisuke
Abstract: Coordination polymers (CPs) with infinite metal–sulfur bond networks have unique electrical conductivities and optical properties. However, the development of new (‐M‐S‐) n ‐structured CPs is hindered by difficulties with their crystallization. Herein, we describe the use of machine learning to optimize the synthesis of trithiocyanuric acid (H3 ttc)‐based semiconductive CPs with infinite Ag−S bond networks, report three CP crystal structures, and reveal that isomer selectivity is mainly determined by proton concentration in the reaction medium. One of the CPs, [Ag2 Httc] n, features a 3D‐extended infinite Ag−S bond network with 1D columns of stacked triazine rings, which, according to first‐principle calculations, provide separate paths for holes and electrons. Time‐resolved microwave conductivity experiments show that [Ag2 Httc] n is highly photoconductive ( φ Σ μ max =1.6×10 −4 cm 2 V −1 s −1 ). Thus, our method promotes the discovery of novel CPs with selective topologies that are difficult to crystallize. Abstract : Semiconductive trithiocyanuric acid (H3 ttc)‐based coordination polymers (CPs) containing Ag−S bonds were synthesized using machine‐learning‐assisted techniques. One of the obtained CPs, [Ag2 Httc] n (KGF‐6), features a three dimensionally extended infinite Ag−S bond network with one‐dimensional column structures of stacked triazine rings. This network structure provides segregated paths for holes and electrons (see picture).