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

¹H NMR: Long-Range Coupling

¹H NMR: Long-Range Coupling

The coupling interactions of nuclei across four or more bonds are usually weak, with J values less than 1 Hz. While these are usually not observed in spectra, the presence of multiple bonds along the coupling pathway can result in observable long-range coupling.
In alkenes, spin information is communicated via σ–π overlap, as seen in allylic (four-bond) and homoallylic (five-bond) couplings. These coupling interactions are stronger when the σ bond is parallel to the alkene...

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...

Thermodynamic Potentials

Thermodynamic Potentials

Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...

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

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...

You might also read

Related Articles

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

Sort by

Same author

Spectral convergence of sum-of-Gaussians tensor neural networks for many-electron Schrödinger equation.

The Journal of chemical physics·2026

Same author

Systematic review of self-management practices for vaginal irrigation among cervical cancer patients undergoing radiotherapy.

Frontiers in oncology·2026

Same author

Crowded Public Spaces as Hotspots of Airborne Microbial Risk: A Population-Weighted Risk Assessment in Urban Environments.

Environmental science & technology·2026

Same author

Nasal Instillation of Complex Metal Oxide Particles Induces Brain Metal Accumulation and Neurobehavioral Toxicity in Mice.

Environmental science & technology·2026

Same author

Accelerating molecular dynamics simulations using fast Ewald summation with prolates.

Nature communications·2026

Same author

Polydopamine-coated triamcinolone acetonide acetate nanocrystals: prevention of postoperative peritoneal adhesion by combining anti-inflammation, oxidative stress scavenging and cytokine adsorption.

Nanoscale·2026

Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026

Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026

Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026

Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026

Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026

Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 12, 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

Machine-Learning Interatomic Potentials for Long-Range Systems.

Yajie Ji¹, Jiuyang Liang^1,2, Zhenli Xu^1,3

¹Shanghai Jiao Tong University, School of Mathematical Sciences, Shanghai 200240, China.

Physical Review Letters

|November 7, 2025

Summary

This summary is machine-generated.

We developed a novel neural network (SOG-Net) to accurately model long-range interactions in machine-learning force fields for molecular simulations. This method enhances accuracy and efficiency for complex systems.

More Related Videos

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

Published on: January 25, 2020

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

Related Experiment Videos

Last Updated: Jan 12, 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

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

Published on: January 25, 2020

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

Area of Science:

Computational Chemistry
Materials Science
Machine Learning

Background:

Machine-learning interatomic potentials offer quantum accuracy at reduced cost for molecular simulations.
Current models often overlook essential long-range interactions, limiting their applicability.

Purpose of the Study:

To introduce a novel framework, the sum-of-Gaussians neural network (SOG-Net), for incorporating long-range interactions into machine-learning force fields.
To enhance the accuracy and scope of molecular simulations by addressing limitations in existing models.

Main Methods:

Developed SOG-Net, a lightweight framework utilizing a latent-variable learning network to connect short- and long-range interactions.
Implemented an efficient Fourier convolution layer for capturing long-range effects.
Employed sum-of-Gaussians multipliers and nonuniform fast Fourier transforms for adaptive decay behavior and near-linear computational complexity.

Main Results:

SOG-Net effectively integrates long-range interactions into machine-learning force fields.
The framework demonstrates adaptive capture of diverse long-range decay behaviors.
Achieved close-to-linear computational complexity during training and simulation.

Conclusions:

SOG-Net provides a versatile and efficient solution for modeling long-range interactions in molecular simulations.
The method is effective across a wide range of systems requiring accurate long-range force field descriptions.