Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Metal-Ligand Bonds02:51

Metal-Ligand Bonds

20.6K
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...
20.6K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

26.2K
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...
26.2K
Valence Bond Theory02:42

Valence Bond Theory

8.5K
Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
8.5K
Complexation Equilibria: The Chelate Effect01:19

Complexation Equilibria: The Chelate Effect

460
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...
460
Complexation Equilibria: Factors Influencing Stability of Complexes01:09

Complexation Equilibria: Factors Influencing Stability of Complexes

341
In complexation reactions, metal cations are the electron pair acceptors, and the ligands are the electron pair donors. The stability of the metal complexes depends primarily on the complexing ability of the central metal ion and the nature of the ligands. Generally, the complexing ability of the metal ion depends on the size and charge of the ion. As the metal ion size increases, the stability of the metal complexes decreases, provided that the valency of the metal ion and the ligands remain...
341
Complexometric Titration: Ligands00:43

Complexometric Titration: Ligands

912
Different monodentate and polydentate ligands are used as complexing agents in complexometric titration reactions. The formation of complexes by mono- and bidentate ligands involves two or more intermediate steps, limiting their use as complexing agents. In comparison, polydentate ligands can form complexes with metal ions in a single-step process, facilitating sharper end points. This means polydentate ligands, such as amino carboxylic acid derivatives, are most commonly employed in...
912

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Side-Chain-Based Cross-Linking of Amorphous Iono-Electronic Conductive Polymers for Thermo-Chemical Stability in Electrochemical Devices.

ACS applied materials & interfaces·2026
Same author

High-Throughput Discovery of Conformation-Switching Mechanophores with Enhanced Reactivity and Stability.

Inorganic chemistry·2026
Same author

Mechanophore cross-linking enhances ballistic energy dissipation of polymers.

Nature·2026
Same author

QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures.

Journal of chemical information and modeling·2026
Same author

Mammalian-like steroidogenesis in plants gives rise to endocrine-mimetic cardenolides.

Science advances·2026
Same author

pyEF: A Python Framework for QM and QM/MM Atom-Wise Electric Field Analysis.

Journal of chemical theory and computation·2026
Same journal

DeepDPM: A Deep Learning Method for MoRFs Prediction Based on Wavelet Transform and Dynamic Convolutional Attention Mechanism.

Journal of chemical information and modeling·2026
Same journal

Graph-Based Generation and Reduction of Complex Chemical Reaction Networks.

Journal of chemical information and modeling·2026
Same journal

Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition.

Journal of chemical information and modeling·2026
Same journal

Machine Learning-Driven Discovery of Indole/Oxoindole-Piperazine Scaffolds as Dual MAO-B/Sig-1R Ligands for Neurodegenerative Disorders.

Journal of chemical information and modeling·2026
Same journal

Mapping Evolution of Molecules across Biochemistry with Assembly Theory.

Journal of chemical information and modeling·2026
Same journal

Structural Proteomics-Based Deciphering of Hydrophobic Packing Fingerprints Informing Protein Thermostability in TIM Barrels.

Journal of chemical information and modeling·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2025

Accessing Valuable Ligand Supports for Transition Metals: A Modified, Intermediate Scale Preparation of 1,2,3,4,5-Pentamethylcyclopentadiene
09:45

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

Published on: March 20, 2017

10.3K

Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery.

Daniel B K Chu1, David A González-Narváez1, Ralf Meyer1

  • 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

Journal of Chemical Information and Modeling
|November 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational model for predicting molecular properties in transition metal complexes, improving accuracy for asymmetric and catalytic systems. The cis interaction model accelerates chemical discovery by enabling faster and more reliable property evaluations.

More Related Videos

Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles
11:54

Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles

Published on: June 25, 2018

10.3K
Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry
16:11

Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry

Published on: June 8, 2022

2.2K

Related Experiment Videos

Last Updated: Jun 6, 2025

Accessing Valuable Ligand Supports for Transition Metals: A Modified, Intermediate Scale Preparation of 1,2,3,4,5-Pentamethylcyclopentadiene
09:45

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

Published on: March 20, 2017

10.3K
Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles
11:54

Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles

Published on: June 25, 2018

10.3K
Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry
16:11

Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry

Published on: June 8, 2022

2.2K

Area of Science:

  • Computational Chemistry
  • Catalysis
  • Materials Science

Background:

  • Accelerating molecular property evaluation is crucial for chemical discovery.
  • Ligand additivity is underutilized in asymmetric transition metal complexes, particularly those with square pyramidal geometries relevant to catalysis.
  • Existing methods lack predictive power beyond simple additivity for complex coordination geometries.

Purpose of the Study:

  • To develop predictive computational methods for molecular properties in asymmetric transition metal complexes.
  • To introduce and validate a novel cis interaction model for enhanced accuracy in predicting catalytic reaction energies.
  • To explore the combination of the cis model with machine learning for further prediction refinement.

Main Methods:

  • Application of a many-body expansion to octahedral and square pyramidal complexes.
  • Development and testing of the cis interaction model, incorporating adjacent ligand effects.
  • Uncertainty analysis to determine the optimal basis set for the model.
  • Integration with Delta-learning for predicting coupled cluster with singles, doubles, and triples (CCSD(T)) reaction energies.

Main Results:

  • The cis interaction model accurately predicts adiabatic spin-splitting energies for octahedral Fe(II) complexes within 1.4 kcal/mol average error.
  • The model infers DFT- and CCSD(T)-calculated catalytic reaction energies within 1 kcal/mol average error.
  • The cis model successfully predicts low-symmetry complexes and demonstrates the potential importance of trans interactions in specific ligand combinations.
  • Combining the cis model with Delta-learning reduces prediction error by approximately 30% compared to using the cis model alone.

Conclusions:

  • The cis interaction model offers a significant advancement in predicting molecular properties for asymmetric transition metal complexes.
  • This method provides a robust framework for accelerating catalyst design and chemical discovery.
  • The integration with Delta-learning further enhances predictive accuracy, paving the way for more efficient computational chemistry workflows.