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

Intermolecular Forces

Intermolecular Forces

Atoms and molecules interact through bonds (or forces): intramolecular and intermolecular. The forces are electrostatic as they arise from interactions (attractive or repulsive) between charged species (permanent, partial, or temporary charges) and exist with varying strengths between ions, polar, nonpolar, and neutral molecules. The different types of intermolecular forces are ion–dipole, dipole–dipole, hydrogen bonds, and dispersion; among these, dipole–dipole, hydrogen...

Van der Waals Interactions

Van der Waals Interactions

Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.

Intermolecular Forces and Physical Properties

Intermolecular Forces and Physical Properties

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.

Intermolecular vs Intramolecular Forces

Intermolecular vs Intramolecular Forces

Intermolecular forces (IMF) are electrostatic attractions arising from charge-charge interactions between molecules. The strength of the intermolecular force is influenced by the distance of separation between molecules. The forces significantly affect the interactions in solids and liquids, where the molecules are close together. In gases, IMFs become important only under high-pressure conditions (due to the proximity of gas molecules). Intermolecular forces dictate the physical properties of...

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

You might also read

Related Articles

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

Sort by

Same author

Intercalated carbon nanotube fibers with high specific electrical conductivity.

Science (New York, N.Y.)·2026

Same author

Accurate density functional theory for noncovalent interactions in charged systems.

Science advances·2026

Same author

Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations.

Journal of chemical theory and computation·2026

Same author

aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials.

Journal of chemical information and modeling·2026

Same author

Correction to "Noncovalent Interactions in Density Functional Theory: All the Charge Density We Do Not See".

Journal of the American Chemical Society·2026

Same author

A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data.

Journal of chemical information and modeling·2026

Search research articles

Related Experiment Video

Updated: Jul 26, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

Efficient interatomic descriptors for accurate machine learning force fields of extended molecules.

Adil Kabylda¹, Valentin Vassilev-Galindo¹, Stefan Chmiela^2,3

¹Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg.

Nature Communications

|June 15, 2023

Summary

This summary is machine-generated.

This study introduces an automated method to create more efficient machine learning force fields (MLFFs) by reducing descriptor features. This approach enhances the accuracy and speed of molecular dynamics simulations for complex systems.

More Related Videos

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

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Related Experiment Videos

Last Updated: Jul 26, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

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

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Area of Science:

Computational chemistry
Materials science
Biophysics

Background:

Machine learning force fields (MLFFs) aim for accurate molecular dynamics (MD) simulations at reduced computational cost.
Current MLFFs face challenges in efficiently describing non-local interactions and reducing descriptor dimensionality for broader applicability.
Predictive MLFF simulations for realistic molecular systems require improved descriptor design.

Purpose of the Study:

To develop an automated approach for reducing interatomic descriptor features in MLFFs.
To simultaneously address the challenges of efficient non-local interaction descriptors and descriptor dimensionality reduction.
To enhance the accuracy, interpretability, and efficiency of MLFFs for molecular simulations.

Main Methods:

An automated feature selection/reduction method was developed and applied to the Gradient-Domain Machine Learning (GDML) framework.
The approach focused on optimizing descriptors for non-local interatomic interactions.
The method was tested on diverse systems including peptides, DNA base pairs, fatty acids, and supramolecular complexes.

Main Results:

The proposed method significantly reduces the number of descriptor features while maintaining MLFF accuracy.
Non-local features, extending up to 15 Å, were found to be critical for preserving accuracy across various molecular systems.
The number of essential non-local features became comparable to local features (below 5 Å) in the optimized descriptors.
The efficiency of MLFFs was increased, with computational cost potentially scaling linearly with system size.

Conclusions:

The developed automated approach effectively reduces descriptor dimensionality in MLFFs.
Accurate molecular dynamics simulations require the inclusion of crucial non-local interatomic interactions.
This work enables the development of global MLFFs with linear scaling computational cost, facilitating larger and more complex simulations.