Failure-Experiment-Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks with Two-Dimensional Secondary Building Units.

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.

Failure-Experiment-Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks with Two-Dimensional Secondary Building Units*.

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.

Machine-Learning-Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons.

Coordination polymers (CPs) with infinite metal-sulfur bond networks have unique electrical conductivities and optical properties. However, the development of new (-M-S-) -structured CPs is hindered by difficulties with their crystallization. Herein, we describe the use of machine learning to optimize the synthesis of trithiocyanuric acid (H ttc)-based semiconductive CPs with infinite Ag-S bond networks, report three CP crystal structures, and reveal that isomer selectivity is mainly determined by proton concentration in the reaction medium.