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 À 4 S cm À 1 for KGF-3(Y)) at a high temperature (363 K) and relative humidity of 95 % RH.

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and 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 high relative humidity (95%).<br>

Invited for the cover of this issue are Daisuke Tanaka at Kwansei Gakuin University and co‐workers at Kwansei Gakuin University, Hokkaido University, Kyoto University, Japan and KU Leuven, Belgium. The image is a depiction of exploring the desired crystal by decision tree analysis. Read the full text of the article at 10.1002/chem.202102404.

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and 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 high relative humidity (95%).<br />

Supporting information for this article is given via a link at the end of the document.