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 Ni4[La74Ni104(IDA)96(OH)184(C2O4)12(H2O)76]·(NO3)38·(H2O)120 (La
74
Ni
104
,
5 × 5 × 3 – C2O4) and [La84Ni132(IDA)108(OH)168(C2O4)24(NO3)12(H2O)116]·(NO3)72·(H2O)296 (La
84
Ni
132
, 5 × 5 × 5 –
C2O4) 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 n × m × l 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 n × m × l inorganic cages through oxalate
ions, which pushes the synthetic methodology study on elusive inorganic
giant cages in a significantly systematic way.