Failure‐Experiment‐Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal‐Organic Frameworks with Two‐Dimensional Secondary Building Units. Issue 66 (5th November 2021)

• Record Type: Journal Article
• Authors: Kitamura, Yu; Terado, Emi; Zhang, Zechen; Yoshikawa, Hirofumi; Inose, Tomoko; Uji‐i, Hiroshi; Tanimizu, Masaharu; Inokuchi, Akihiro; Kamakura, Yoshinobu; Tanaka, Daisuke
• Is Part Of: Chemistry. Volume 27:Issue 66(2021)
• Page Start: 16347
• Page End: 16353
• Publication Date: 2021-11-05
• Subjects: lanthanides -- machine learning -- metal-organic frameworks -- proton conductivity -- solvothermal synthesis; Chemistry -- Periodicals; 540
• DOI: 10.1002/chem.202102404
• Languages: English
• ISSNs: 0947-6539

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. Abstract : Synthesis analysis : A series of novel metal–organic frameworks with lanthanide double‐layer‐based inorganic subnetworks (KGF‐3) has been synthesized assisted by machine learning. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF‐3 synthesis by using two machine‐learning techniques; cluster analysis and decision‐tree analysis. KGF‐3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities at high temperature (363 K) and a relative humidity of 95 % RH.