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

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 (H3ttc)‐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, [Ag2Httc]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 [Ag2Httc]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.

Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr

“…It is noteworthy that the machine-learning model imbued with Cs/Sr coordination chemistry knowledge could also be a proof of concept, as the same methodology may be applicable to other coordination processes in the grand scheme of SNF reprocessing in terms of the identification of new ligands and/or comparison among candidates. We note that, in the realm of organic chemistry, machine learning has greatly automated, enhanced, and accelerated synthetic efforts, leading to efficient and important discoveries. − In addition, the application of machine learning in inorganic chemistry has also made valuable contributions to theoretical studies and synthetic efforts. − …”

Coordination polymers as precursors of simple and complex oxides

“…They have been an essential topic in materials chemistry for decades. [13][14][15][16] CPs have often been described as twoor three-dimensional crystals. However, amorphous CPs (amorphous, liquid, and glass) have recently been getting more attention as novel functional materials.…”

Bayesian optimization of the composition of the lanthanide metal–organic framework MIL-103 for white-light emission

“…In recent years, the field of material informatics has gained significant attention and has been actively studied to facilitate the discovery of novel materials. 18,20–29 In particular, Bayesian optimization predicts an unknown function and suggests the next search condition where the probability of showing a maximum or minimum value is high. 30 Bayesian optimization methods are useful tools for yield improvement and composition optimization.…”