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Machine Learning Guided Assembly of a Nested Gd20@Gd32 @Ni36 Cluster via Urea Controlled Carbonate Release
Ming-Qiang Qi1, Rui-Le Yang1, Jing-Zhou Wu1
1Collaborative Innovation Center of Chemistry for Energy Materials, State Key Laboratory of Physical Chemistry of Solid Surfaces and Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
Angewandte Chemie (International Ed. in English)
|September 2, 2025
Summary
Researchers synthesized a complex, nested lanthanide cluster using urea as a carbonate source. Machine learning accelerated the discovery of this novel Gd52Ni36 architecture, showcasing data-driven synthesis strategies.
Area of Science:
- Inorganic Chemistry
- Materials Science
- Nanotechnology
Background:
- Lanthanide-based polyhedral clusters exhibit unique geometries and properties.
- Controlled synthesis of these complex structures presents significant challenges.
Purpose of the Study:
- To report the controlled synthesis of a novel nested three-shell lanthanide cluster.
- To explore data-driven strategies for accelerating the discovery of complex lanthanide architectures.
Main Methods:
- Utilized urea as a slow-release carbonate source to template the inner shell.
- Employed single-crystal X-ray diffraction to determine the cluster's structure (Gd20@Gd32@Ni36).
- Implemented a machine learning-guided, high-throughput synthesis platform to explore reaction conditions.
Main Results:
- Successfully synthesized a nested three-shell cluster: [Gd52Ni36(MeIDA)36(OH)114(CO3)12(H2O)48]·(ClO4)18·(H2O)21.
- Revealed a Gd20@Gd32@Ni36 arrangement with a Platonic dodecahedron-shaped Gd20 inner core templated by carbonate.
- Identified key parameters and phase boundaries governing cluster formation through 780 explored conditions.
Conclusions:
- Demonstrated the effectiveness of urea as a templating agent for dodecahedral shells in lanthanide clusters.
- Validated the power of machine learning and high-throughput synthesis in accelerating the discovery of complex inorganic materials.
- Opened new avenues for designing and synthesizing intricate lanthanide-based architectures.
Keywords:
Automated synthesisCarbonate templatingLanthanide clustersMachine learningPlatonic polyhedraMore Related Videos
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