Machine-Learning-Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons.

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 (φΣµmax =1.6×10-4  cm2 V-1 s-1 ). Thus, our method promotes the discovery of novel CPs with selective topologies that are difficult to crystallize.