Failure‐Experiment‐Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal‐Organic Frameworks with Two‐Dimensional Secondary Building Units**
Yu Kitamura, Emi Terado, Zechen Zhang, Hirofumi Yoshikawa, Tomoko Inose, Hiroshi Uji‐i, Masaharu Tanimizu, Akihiro Inokuchi, Yoshinobu Kamakura, Daisuke Tanaka
Abstract
Abstract Novel metal–organic frameworks containing lanthanide double‐layer‐based secondary building units (KGF‐3) were synthesized by using machine learning (ML). Isolating pure KGF‐3 was challenging, and the synthesis was not reproducible because impurity phases were frequently obtained under the same synthetic conditions. Thus, dominant factors for the synthesis of KGF‐3 were identified, and its synthetic conditions were optimized by using two ML techniques. Cluster analysis was used to classify the obtained powder X‐ray diffractometry patterns of the products and thus automatically determine whether the experiments were successful. Decision‐tree analysis was used to visualize the experimental results, after extracting factors that mainly affected the synthetic reproducibility. Water‐adsorption isotherms revealed that KGF‐3 possesses unique hydrophilic pores. Impedance measurements demonstrated good proton conductivities ( σ =5.2×10 −4 S cm −1 for KGF‐3(Y)) at a high temperature (363 K) and relative humidity of 95 % RH.