Exploration and Insights on Topology Adjustment of Giant Heterometallic Cages Featuring Inorganic Skeletons Assisted by Machine Learning.

Inorganic molecular cages are emerging multifunctional molecular-based platforms with the unique merits of rigid skeletons and inherited properties from constituent metal ions. However, the sensitive coordination bonds and vast synthetic space have limited their systematic exploration. Herein, two giant cage-like clusters featuring the organic ligand-directed inorganic skeletons of Ni[LaNi(IDA)(OH)(CO)(HO)]·(NO)·(HO) (, 5 × 5 × 3 - CO) and [LaNi(IDA)(OH)(CO)(NO)(HO)]·(NO)·(HO) (, 5 × 5 × 5 - CO) were discovered by a high-throughput synthetic search. With the assistance of machine learning analysis of the experimental data, phase diagrams of the two clusters in a four-parameter synthetic space were depicted. The effect of alkali, oxalate, and other parameters on the formation of clusters and the mechanism regulating the size of two × × clusters were elucidated. This work uses high-throughput synthesis and machine learning methods to improve the efficiency of 3d-4f cluster discovery and finds the highest-nuclearity 3d-4f cluster to date by regulating the size of the × × inorganic cages through oxalate ions, which pushes the synthetic methodology study on elusive inorganic giant cages in a significantly systematic way.