Machine–Learning–Assisted Synthesis of Novel Ag-S Coordination Polymer Using Failed Experiments

Abstract

Coordination Polymer (CP) exhibit promising various functionalities by utilizing their uniform framework structures. Most reported CPs are composed of oxygen or nitrogen as coordination atoms in the metal bridging part, and there are few structures containing sulfur or other elements as coordination atoms. Since mechanism of crystallization process of CP is not fully understood and rational design strategy for novel CPs has not been established, time consuming exploration has been required to optimize the synthesis conditions. Here, we focus on machine learning techniques, cluster analysis and decision tree analysis, to improve the accuracy of the prediction for the synthesis conditions. In this work, we explored the synthesis conditions of CP containing sulfide-metal bonds by high throughput screening systems. We implemented cluster analysis to categorize their powder X-ray diffraction (PXRD) patterns into several different types. The relationship between synthesis condition and obtained PXRD diffraction patterns was estimated by decision tree analysis. We have demonstrated that cluster analysis and decision tree are useful to predict the reaction mechanism and explore synthesis condition for novel CPs.