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

Molecular Comparison of Gases, Liquids, and Solids02:26

Molecular Comparison of Gases, Liquids, and Solids

51.5K
Particles in a solid are tightly packed together (fixed shape) and often arranged in a regular pattern; in a liquid, they are close together with no regular arrangement (no fixed shape); in a gas, they are far apart with no regular arrangement (no fixed shape). Particles in a solid vibrate about fixed positions (cannot flow) and do not generally move in relation to one another; in a liquid, they move past each other (can flow) but remain in essentially constant contact; in a gas, they move...
51.5K
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

19.3K
Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
19.3K
Intermolecular Forces and Physical Properties02:56

Intermolecular Forces and Physical Properties

24.9K
24.9K
Intermolecular Forces03:13

Intermolecular Forces

66.7K
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...
66.7K
Intermolecular Forces in Solutions02:28

Intermolecular Forces in Solutions

37.3K
The formation of a solution is an example of a spontaneous process, a process that occurs under specified conditions without energy from some external source.
When the strengths of the intermolecular forces of attraction between solute and solvent species in a solution are no different than those present in the separated components, the solution is formed with no accompanying energy change. Such a solution is called an ideal solution. A mixture of ideal gases (or gases such as helium and argon,...
37.3K
Structures of Solids02:22

Structures of Solids

16.9K
Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
16.9K

You might also read

Related Articles

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

Sort by
Same author

Accuracy and limitations of machine-learned interatomic potentials for magnetic systems: A case study on Fe-Cr-C.

Physical review. E·2025
Same author

Machine-learning interatomic potential for barium sulfide: From thermodynamic properties to crystal growth kinetics.

The Journal of chemical physics·2025
Same author

Transfer learning for accurate description of atomic transport in Al-Cu melts.

The Journal of chemical physics·2024
Same author

Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulations.

The Journal of chemical physics·2023
Same author

Freezing in two-length-scale systems: complexity, universality and prediction.

Journal of physics. Condensed matter : an Institute of Physics journal·2022
Same author

Structure of the simple harmonic-repulsive system in liquid and glassy states studied by the triple correlation function.

Journal of physics. Condensed matter : an Institute of Physics journal·2020

Related Experiment Video

Updated: Nov 25, 2025

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid
08:54

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

5.8K

Deep machine learning interatomic potential for liquid silica.

I A Balyakin1,2, S V Rempel2,3, R E Ryltsev1,4,5

  • 1Institute of Metallurgy of the Ural Branch of the Russian Academy of Sciences, 620016, Ekaterinburg, Russia.

Physical Review. E
|December 17, 2020
PubMed
Summary

Machine learning, using deep potential molecular dynamics (DeePMD), creates accurate neural network potentials (NNPs) for silica simulations. A small dataset accurately predicts liquid and glassy silica

More Related Videos

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.8K
Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

10.7K

Related Experiment Videos

Last Updated: Nov 25, 2025

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid
08:54

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

5.8K
Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.8K
Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

10.7K

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Neural Network Potentials (NNPs) offer a balance between accuracy and efficiency in materials simulations.
  • Developing accurate NNPs requires well-prepared training datasets of ab initio trajectories.
  • Silica serves as a model system for network-forming liquids and glasses.

Purpose of the Study:

  • To develop and validate a Neural Network Potential (NNP) for silica using the Deep Potential Molecular Dynamics (DeePMD) approach.
  • To assess the accuracy of the developed NNP in describing the structural and dynamical properties of liquid and glassy silica.
  • To demonstrate the capability of NNPs to extend simulation time-space scales for materials properties.

Main Methods:

  • Application of the Deep Potential Molecular Dynamics (DeePMD) method.
  • Training an NNP using a relatively small dataset of high-temperature ab initio configurations.
  • Calculation and comparison of structural properties (pair correlation functions, angular distribution) and dynamical properties (velocity autocorrelation functions, vibrational density of states, mean-square displacement) against ab initio data.

Main Results:

  • A small training dataset of high-temperature configurations was sufficient to develop an accurate NNP for silica.
  • The developed NNP accurately reproduces structural and dynamical properties of liquid silica, showing close agreement with ab initio data.
  • The NNP effectively describes the structure of glassy silica, even without low-temperature configurations in the training set.
  • NNP simulations significantly extend accessible time-space scales, enabling more accurate calculation of dynamical and transport properties.

Conclusions:

  • Deep Potential Molecular Dynamics (DeePMD) provides an efficient method for developing accurate NNPs for glass-forming materials like silica.
  • NNPs enable large-scale, accurate simulations of liquids and glasses, overcoming limitations of traditional ab initio methods.
  • The developed NNP for silica opens new avenues for simulating complex structural and dynamical behaviors in disordered materials.