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Related Concept Videos

Properties of Transition Metals02:58

Properties of Transition Metals

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Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
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Properties of Organometallic Compounds01:23

Properties of Organometallic Compounds

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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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Oxidation Numbers03:14

Oxidation Numbers

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In redox reactions, the transfer of electrons occurs between reacting species. Electron transfer is described by a hypothetical number called the oxidation number (or oxidation state). It represents the effective charge of an atom or element, which is assigned using a set of rules.
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Coordination Number and Geometry02:57

Coordination Number and Geometry

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For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
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Coordination Compounds and Nomenclature02:54

Coordination Compounds and Nomenclature

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In most main group element compounds, the valence electrons of the isolated atoms combine to form chemical bonds that satisfy the octet rule. For instance, the four valence electrons of carbon overlap with electrons from four hydrogen atoms to form CH4. The one valence electron leaves sodium and adds to the seven valence electrons of chlorine to form the ionic formula unit NaCl (Figure 1a). Transition metals do not normally bond in this fashion. They primarily form coordinate covalent bonds, a...
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Corrosion02:49

Corrosion

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The degradation of metals due to natural electrochemical processes is known as corrosion. Rust formation on iron, tarnishing of silver, and the blue-green patina that develops on copper are examples of corrosion. Corrosion involves the oxidation of metals. Sometimes it is protective, such as the oxidation of copper or aluminum, wherein a protective layer of metal oxide or its derivatives forms on the surface, protecting the underlying metal from further oxidation. In other cases, corrosion is...
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Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
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High Structural Error Rates in "Computation-Ready" MOF Databases Discovered by Checking Metal Oxidation States.

Andrew J White1, Marco Gibaldi1, Jake Burner1

  • 1Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Canada K1N 9A4.

Journal of the American Chemical Society
|May 16, 2025
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Summary
This summary is machine-generated.

Many metal-organic framework (MOF) databases contain chemically invalid structures. Our MOSAEC algorithm accurately detects these errors, improving materials discovery through computational screening.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Crystallography

Background:

  • Computation-ready databases are crucial for high-throughput screening (HTS) and machine learning in materials discovery.
  • The structural integrity of metal-organic framework (MOF) databases is largely unquantified, potentially impacting HTS and ML model reliability.
  • Existing MOF databases may contain chemically invalid structures, leading to inaccurate computational screening results.

Purpose of the Study:

  • To introduce MOSAEC, a novel algorithm for detecting chemically invalid MOF structures based on metal oxidation states.
  • To quantify the prevalence of structural errors in leading MOF databases and HTS studies.
  • To improve the reliability of computational materials discovery using MOFs.

Main Methods:

  • Development of the MOSAEC algorithm to identify chemically invalid MOF structures by analyzing metal oxidation states.
  • Manual validation of MOSAEC against 14,796 MOF structures from the CoRE database.
  • Examination of over 1.9 million structures across 14 leading MOF databases and analysis of structures from 8 HTS studies.

Main Results:

  • MOSAEC achieved 96% accuracy in detecting chemically invalid MOF structures during manual validation.
  • Structural error rates exceeding 40% were found in most of the 14 leading MOF databases analyzed.
  • Analysis revealed that 52% of top-performing MOF candidates from recent HTS studies were chemically invalid.

Conclusions:

  • The MOSAEC algorithm effectively identifies chemically invalid MOF structures, enhancing data quality for computational screening.
  • A significant proportion of MOF structures in leading databases and HTS studies contain errors, necessitating data validation.
  • Improving the structural fidelity of MOF databases is critical for reliable materials discovery via HTS and machine learning.