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Related Concept Videos

Properties of Transition Metals02:58

Properties of Transition Metals

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.
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.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
Complexation Equilibria: The Chelate Effect01:19

Complexation Equilibria: The Chelate Effect

In complexation reactions, metal atoms or cations interact with ligands to form donor-acceptor adducts called metal complexes. Ligands that bind through one donor site are monodentate, ligands with two donor sites are bidentate, and those with more than two donor sites are polydentate ligands. For example, ethylene diamine is a bidentate ligand that binds through two nitrogen donor atoms, forming a five-membered ring. EDTA is a polydentate ligand that binds through four oxygen and two nitrogen...
Properties of Organometallic Compounds01:23

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.
Ladder Diagrams: Complexation Equilibria01:07

Ladder Diagrams: Complexation Equilibria

Ladder diagrams are useful for evaluating equilibria involving metal-ligand complexes. The vertical scale of the ladder diagram represents the concentration of unreacted or free ligand, pL. The horizontal lines on the scale depict the log of stepwise formation constants for metal-ligand complexes and indicate the dominant species in all the regions.
The formation constant, K1, for the formation of Cd(NH3)2+ complex from cadmium and ammonia is 3.55 × 102. Log K1 (i.e. pNH3) is 2.55, and...

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Related Experiment Video

Updated: Jun 25, 2026

Thermochemical Studies of Ni(II) and Zn(II) Ternary Complexes Using Ion Mobility-Mass Spectrometry
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Thermochemical Studies of Ni(II) and Zn(II) Ternary Complexes Using Ion Mobility-Mass Spectrometry

Published on: June 8, 2022

tmQM-RDF Data Set: A Knowledge Graph Representing Transition Metal Complexes.

Luca Cibinel1,2, Trond Linjordet3,4, Johan Pensar1,2

  • 1Integreat─Norwegian Centre for Knowledge-driven Machine Learning, 0851 Oslo, Norway.

Journal of Chemical Information and Modeling
|June 24, 2026
PubMed
Summary

A new dataset, transition metal quantum mechanics RDF (tmQM-RDF), offers detailed descriptions of 60,000 transition metal complexes. This knowledge graph aids machine learning in chemistry, facilitating the study of these valuable compounds.

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Accessing Valuable Ligand Supports for Transition Metals: A Modified, Intermediate Scale Preparation of 1,2,3,4,5-Pentamethylcyclopentadiene
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Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks

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Last Updated: Jun 25, 2026

Thermochemical Studies of Ni(II) and Zn(II) Ternary Complexes Using Ion Mobility-Mass Spectrometry
16:11

Thermochemical Studies of Ni(II) and Zn(II) Ternary Complexes Using Ion Mobility-Mass Spectrometry

Published on: June 8, 2022

Accessing Valuable Ligand Supports for Transition Metals: A Modified, Intermediate Scale Preparation of 1,2,3,4,5-Pentamethylcyclopentadiene
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Accessing Valuable Ligand Supports for Transition Metals: A Modified, Intermediate Scale Preparation of 1,2,3,4,5-Pentamethylcyclopentadiene

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Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks

Published on: June 9, 2023

Area of Science:

  • Chemistry
  • Materials Science
  • Computational Science

Background:

  • Transition metal complexes (TMCs) are crucial in various chemical applications, including catalysis and medicinal chemistry.
  • Studying TMCs requires comprehensive data for effective computational analysis and machine learning.
  • Existing datasets may lack the detailed qualitative and quantitative information needed for advanced research.

Purpose of the Study:

  • To introduce the transition metal quantum mechanics RDF (tmQM-RDF) dataset, a novel knowledge graph for TMCs.
  • To provide a user-friendly Python package (tmqmrdfdata) for accessing and utilizing the dataset.
  • To demonstrate the utility of tmQM-RDF in TMC manipulation tasks using machine learning.

Main Methods:

  • Constructed a knowledge graph using the Resource Description Framework (RDF) vocabulary.
  • Collected and curated detailed descriptions for approximately 60,000 TMCs.
  • Developed the tmqmrdfdata Python package for data access and manipulation.

Main Results:

  • The tmQM-RDF dataset contains rich, detailed descriptions of ~60k TMCs, including compositional and molecular graph information.
  • The tmqmrdfdata package offers a user-friendly interface to this extensive knowledge graph.
  • Exploiting tmQM-RDF for TMC manipulation tasks showed promising performance with simple probabilistic models.

Conclusions:

  • The tmQM-RDF dataset represents a significant contribution to data modeling for transition metal complexes.
  • Accessible data and user-friendly tools like tmqmrdfdata can accelerate machine learning applications in TMC research.
  • The dataset's rich information enables effective TMC analysis and manipulation, even with basic models.