Machine‐Learning‐Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons
作者:Takuma Wakiya、Yoshinobu Kamakura、Hiroki Shibahara、Kazuyoshi Ogasawara、Akinori Saeki、Ryosuke Nishikubo、Akihiro Inokuchi、Hirofumi Yoshikawa、Daisuke Tanaka
DOI:10.1002/anie.202110629
日期:2021.10.18
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
具有无限金属硫键网络的配位聚合物 (CP) 具有独特的导电性和光学特性。然而,新的 (-MS-) n结构 CP 的开发受到其结晶困难的阻碍。在此,我们描述了使用机器学习来优化具有无限 Ag-S 键网络的基于三硫氰尿酸 (H 3 ttc) 的半导体 CP的合成,报告了三种 CP 晶体结构,并揭示了异构体的选择性主要取决于质子浓度在反应介质中。CP 之一,[Ag 2 Httc] n,具有 3D 扩展的无限 Ag-S 键网络,具有堆叠的三嗪环的 1D 列,根据第一性原理计算,其为空穴和电子提供单独的路径。时间分辨微波电导率实验表明[Ag 2 Httc] n是高度光导的(φ Σ μ max =1.6×10 -4 cm 2 V -1 s -1)。因此,我们的方法促进了具有难以结晶的选择性拓扑结构的新型 CP 的发现。