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Machine Learning-Assisted Rapid Scalable Synthesis and Assembly Evolution of Lithographic Metal-Oxo Clusters.

Huifang Zhao1, Siming Qi1, Zuohu Zhou1

  • 1Institute of Modern Optics, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.

Journal of the American Chemical Society
|June 30, 2026
PubMed
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This summary is machine-generated.

Machine learning accelerates metal-oxo cluster (MOC) synthesis. A novel Sn12Ti4 cluster was rapidly produced at room temperature, demonstrating scalable production and diverse applications, including high-resolution lithography.

Area of Science:

  • Inorganic Chemistry
  • Materials Science
  • Machine Learning Applications

Background:

  • Controllable synthesis of metal-oxo clusters (MOCs) is challenging due to complex reaction pathways and coupled variables, limiting efficiency and scalability.
  • Existing methods struggle with precise control over intricate reaction systems required for MOC production.

Purpose of the Study:

  • To develop a machine-learning-assisted strategy for optimizing reaction spaces in MOC synthesis.
  • To enable precise control over complex reaction systems for efficient and scalable MOC production.
  • To synthesize and characterize a novel tin-titanium metal-oxo cluster (Sn12Ti4).

Main Methods:

  • Employed a machine-learning-assisted reaction-space optimization strategy for data-driven modeling and parameter identification.

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  • Utilized mass spectrometry and theoretical calculations to confirm the structure of the synthesized cluster.
  • Investigated ligand modulation to control cluster assembly into 2D layers, 3D networks, and metal-organic frameworks (MOFs).

    • Main Results:

    • Achieved rapid, room-temperature synthesis of a novel racemic Sn12Ti4 cluster within seconds.
    • Obtained 266 g of phase-pure crystalline Sn12Ti4 within 15 minutes, demonstrating scalability.
    • Demonstrated the assembly of Sn12Ti4 units into various supramolecular structures and homochiral MOFs.
    • Showcased lithographic applications of the Sn12Ti4 cluster with a high resolution of 9.83 nm.

    Conclusions:

    • Established a generalizable machine-learning-assisted route for rapid and scalable synthesis of functional MOCs.
    • The developed strategy provides precise control over complex reaction systems, overcoming previous synthetic limitations.
    • The synthesized Sn12Ti4 cluster exhibits potential for advanced materials applications, including high-resolution lithography.