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Metal-Ligand Bonds02:51

Metal-Ligand Bonds

The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
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Properties of Organometallic Compounds

Organometallic compounds are compounds that contain a carbon–metal bond. Carbon belongs to an organyl group like alkyl, aryl, allyl, or benzyl groups. The metal can be from Group I or Group II of the periodic table, a transition metal, or a semimetal.

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Related Experiment Video

Updated: Jul 1, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

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Leveraging Machine Learning for Metal-Organic Frameworks: A Perspective.

Hongjian Tang1,2, Lunbo Duan1, Jianwen Jiang2

  • 1Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy & Environment, Southeast University, Nanjing 210096, China.

Langmuir : the ACS Journal of Surfaces and Colloids
|November 3, 2023
PubMed
Summary

Machine learning (ML) accelerates the discovery and design of metal-organic frameworks (MOFs). ML efficiently predicts MOF properties and uncovers structure-property relationships, overcoming the limitations of traditional methods.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Chemical Engineering

Background:

  • Metal-organic frameworks (MOFs) offer tunable structures and properties, leading to over 100,000 synthesized and millions of hypothetical candidates.
  • The vast chemical space of MOFs makes identifying optimal materials for specific applications challenging using conventional experimental or simulation approaches.

Purpose of the Study:

  • To review the transformative impact of machine learning (ML) on MOF discovery, design, and synthesis.
  • To highlight ML's capability in predicting MOF properties and deriving structure-property relationships.
  • To discuss current challenges and future opportunities for ML in advancing MOF development.

Main Methods:

  • Leveraging machine learning (ML) algorithms for MOF property prediction.
  • Utilizing abundant experimental and simulation data for ML model training.
  • Analyzing data acquisition, featurization, and model training aspects in ML-driven MOF research.

Main Results:

  • ML has significantly accelerated MOF discovery and design processes.
  • ML enables efficient and accurate prediction of MOF properties.
  • ML facilitates the quantitative derivation of structure-property relationships for rational MOF design.

Conclusions:

  • Machine learning is revolutionizing the field of metal-organic frameworks.
  • ML overcomes the limitations of traditional methods in navigating the immense MOF chemical space.
  • Future ML exploration holds significant promise for accelerating the development of novel MOFs.