Machine–Learning–Assisted Exploration for Synthesis Condition of Sulfido-Cluster Metal–Organic Frameworks

Abstract

MOF crystal can be regarded as an inorganic nanocluster integrated structure. By utilizing such MOF materials, it is possible to achieve a highly self-organized structure that is difficult to achieve by simple assembly of inorganic nanomaterials, and excellent optical properties and carrier mobility can be expected. In this presentation, we report the electronic properties of MOFs that have high-dimensional cluster structures in the framework. In addition, we propose a method based on machine learning technique to improve the accuracy of the prediction for the synthesis condition of MOFs. In this work, we explored synthesis condition of MOFs containing sulfide-metal bonds by high throughput screening systems. We tried to synthesize a MOF composed of trithiocyanuric acid as sulfide containing ligand and Ag ion as metal. The relationship between synthesis condition and obtained X-ray diffraction patterns was estimated by decision tree analysis. In addition, we have succeeded in determining the crystal structure of three novel MOFs.