Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Optimal Foraging

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

Molecular Shapes

Molecular Shapes

Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
Two regions of electron density in a diatomic...

Molecular Geometry and Dipole Moments

Molecular Geometry and Dipole Moments

The VSEPR theory can be used to determine the electron pair geometries and molecular structures as follows:

Molecular Kinetic Energy

Molecular Kinetic Energy

The word "gas" comes from the Flemish word meaning "chaos," first used to describe vapors by the chemist J. B. van Helmont. Consider a container filled with gas, with a continuous and random motion of molecules. During collisions, the velocity component parallel to the wall is unchanged, and the component perpendicular to the wall reverses direction but does not change in magnitude. If the molecule’s velocity changes in the x-direction, then its momentum is changed. During the short time of the...

You might also read

Related Articles

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

Sort by

Same author

From Atomic Interactions to Molecular Miscibility and Philicity: Deciphering Enthalpic Driving Forces.

The journal of physical chemistry. A·2026

Same author

Bridging Atomistic and Mesoscale Lithium Transport via Machine-Learned Force Fields and Markov State Models.

Journal of chemical theory and computation·2026

Same author

Molecular Origins of Philicity: How Atomic Interactions Determine Miscibility and Diffusivity.

Chemphyschem : a European journal of chemical physics and physical chemistry·2026

Same author

Efficient Calculation of Electrostatic Energies for Large-Scale Nonadiabatic Molecular Dynamics in a Site Basis.

Journal of chemical theory and computation·2025

Same author

Representative Random Sampling of Chemical Space.

Journal of chemical theory and computation·2025

Same author

Intrinsic dimensionality of molecular properties.

The Journal of chemical physics·2025

Search research articles

Related Experiment Video

Updated: May 16, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Foraging on the potential energy surface: a swarm intelligence-based optimizer for molecular geometry.

Christoph Wehmeyer¹, Guido Falk von Rudorff, Sebastian Wolf

¹Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany.

The Journal of Chemical Physics

|November 28, 2012

Summary

We developed a novel artificial bee colony (ABC) algorithm to find the lowest energy structures for molecular clusters. This swarm intelligence approach efficiently predicts global minima on potential energy surfaces.

More Related Videos

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Related Experiment Videos

Last Updated: May 16, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Computational chemistry
Materials science
Swarm intelligence

Background:

Predicting the global minimum energy state of molecular clusters is crucial for understanding their properties.
Traditional optimization methods can struggle with the complex, multi-dimensional potential energy landscapes of clusters.
Swarm intelligence offers a bio-inspired alternative for complex optimization problems.

Purpose of the Study:

To introduce a modified artificial bee colony (ABC) algorithm for global geometry optimization of molecular clusters.
To assess the performance of this swarm intelligence approach in finding global minima on potential energy surfaces.
To demonstrate the algorithm's applicability to a range of cluster sizes and interatomic potentials.

Main Methods:

Development of a modified artificial bee colony (ABC) algorithm, a swarm intelligence optimization technique.
Application of the modified ABC algorithm to predict global minima for molecular cluster structures.
Testing the algorithm on clusters containing 2-57 particles with various interatomic potentials.

Main Results:

The modified ABC algorithm successfully predicted global minima for molecular cluster structures.
The algorithm demonstrated robust performance across different cluster sizes (2-57 particles).
The approach proved effective for various interatomic interaction potentials.

Conclusions:

The swarm intelligence-based ABC algorithm is a viable and effective tool for global geometry optimization of molecular clusters.
This method provides an efficient strategy for exploring potential energy surfaces and identifying stable cluster configurations.
The algorithm's adaptability to different potentials and cluster sizes highlights its potential for broader applications in computational chemistry and materials science.