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

25.4K
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...
25.4K
Metallic Solids02:37

Metallic Solids

21.3K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
21.3K
Bonding in Metals02:32

Bonding in Metals

55.7K
Metallic bonds are formed between two metal atoms. A simplified model to describe metallic bonding has been developed by Paul Drüde called the “Electron Sea Model”. 
55.7K
Properties of Organometallic Compounds01:23

Properties of Organometallic Compounds

2.0K
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.
2.0K
Theory of Metallic Conduction01:17

Theory of Metallic Conduction

1.9K
The conduction of free electrons inside a conductor is best described by quantum mechanics. However, a classical model makes predictions close to the results of quantum mechanics. It is called the theory of metallic conduction.
In this theory, Newton's second law of motion is used to determine the acceleration of an electron in the presence of an applied electric field. Then, its velocity is expressed via this acceleration.
An electron moves through the crystal, containing positive ions,...
1.9K
Metal-Semiconductor Junctions01:24

Metal-Semiconductor Junctions

1.3K
The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
Schottky Barriers
Schottky barriers arise when a metal with a work function (Φm) contacts a semiconductor with a different work function (Φs). Initially, electrons transfer until the Fermi levels of the metal and semiconductor align at equilibrium. For instance, if Φm > Φs, the semiconductor Fermi level is higher than the metal's before contact. The...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Quantum-Centric Alchemical Free Energy Calculations.

Journal of chemical theory and computation·2026
Same author

An N-terminal amphipathic helix governs activity and conformational dynamics of Nramp metal transporters.

The Journal of biological chemistry·2026
Same author

An N-terminal amphipathic helix governs activity and conformational dynamics of Nramp metal transporters.

bioRxiv : the preprint server for biology·2026
Same author

Molecular Quantum Computations on a Protein.

Journal of chemical theory and computation·2026
Same author

Advancing Reproducibility and Open Data in Theoretical and Computational Chemistry.

Journal of chemical theory and computation·2026
Same author

Zinc-induced exposure of an LxL motif drives clathrin-mediated endocytosis of the zinc transceptor ZIP4.

The Journal of biological chemistry·2026
Same journal

Correction to "AstraMEV (AI-Guided Structural Assembly of Multi-Epitope Vaccines) Against Infectious Bronchitis Virus".

Journal of chemical information and modeling·2026
Same journal

MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations.

Journal of chemical information and modeling·2026
Same journal

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Journal of chemical information and modeling·2026
Same journal

Structural and Thermodynamic Discrimination between Agonists and Antagonists of Retinoic Acid Receptor γ and the Vitamin D Receptor.

Journal of chemical information and modeling·2026
Same journal

PACEff Builder: An Efficient Platform for Constructing PACE Hybrid-Resolution Models for Molecular Dynamics Simulations of Aqueous Protein, Peptide Assembly, and Membrane Protein Systems.

Journal of chemical information and modeling·2026
Same journal

TransKla: A Local-Global Cross-Attention Based Transformer Approach for Prediction of Lysine Lactylation Sites.

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

Related Experiment Video

Updated: Mar 25, 2026

A Salt-Templated Synthesis Method for Porous Platinum-based Macrobeams and Macrotubes
13:08

A Salt-Templated Synthesis Method for Porous Platinum-based Macrobeams and Macrotubes

Published on: May 18, 2020

9.8K

MCPB.py: A Python Based Metal Center Parameter Builder.

Pengfei Li1, Kenneth M Merz1

  • 1Department of Chemistry, Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan 48824-1322, United States.

Journal of Chemical Information and Modeling
|February 26, 2016
PubMed
Summary
This summary is machine-generated.

MCPB.py is a Python tool that simplifies creating force fields for metal complex simulations. This program efficiently models metal ions, bridging quantum mechanics and molecular dynamics for researchers.

More Related Videos

Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR
14:44

Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

Published on: December 16, 2013

10.1K
Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
08:32

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

13.1K

Related Experiment Videos

Last Updated: Mar 25, 2026

A Salt-Templated Synthesis Method for Porous Platinum-based Macrobeams and Macrotubes
13:08

A Salt-Templated Synthesis Method for Porous Platinum-based Macrobeams and Macrotubes

Published on: May 18, 2020

9.8K
Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR
14:44

Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

Published on: December 16, 2013

10.1K
Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
08:32

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

13.1K

Area of Science:

  • Computational Chemistry
  • Biophysics
  • Materials Science

Background:

  • Simulating metal ions in complexes is challenging due to complex parameterization.
  • Existing methods require extensive manual input and are time-consuming.
  • Accurate force fields are crucial for molecular dynamics simulations of metal-containing systems.

Purpose of the Study:

  • To introduce MCPB.py, an optimized Python-based tool for building metal center force fields.
  • To streamline the process of parameterizing metal ions for molecular dynamics.
  • To facilitate the simulation of diverse metal ion-containing complexes.

Main Methods:

  • Development of MCPB.py with an optimized code structure.
  • Integration with AMBER force fields and support for over 80 metal ions.
  • Implementation of various parametrization schemes for force constants and charges.
  • Demonstration using metalloprotein and organometallic compound examples.

Main Results:

  • MCPB.py significantly reduces the steps required for force field generation compared to previous versions.
  • The tool successfully builds reliable force fields for different metal ion complexes, as shown by case studies.
  • It supports a wide range of metal ions and AMBER force fields.

Conclusions:

  • MCPB.py offers an efficient and accessible solution for metal ion modeling in molecular simulations.
  • The program bridges quantum mechanical calculations and molecular dynamics, simplifying complex simulations.
  • It empowers researchers to overcome difficulties in simulating metal ions across various applications.