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 and Ionic Solids02:54

Molecular and Ionic Solids

16.9K
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...
16.9K
Electrostatic Boundary Conditions in Dielectrics01:27

Electrostatic Boundary Conditions in Dielectrics

1.1K
When an electric field passes from one homogeneous medium to another, crossing the boundary between the two mediums imparts a discontinuity in the electric field. This results in electrostatic boundary conditions that depend on the type of mediums the field propagates through.
Consider a case where both the mediums across a boundary are two different dielectric materials. Recall that the electric field and electric displacement are proportional and related through the material's...
1.1K
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

31.8K
sp3d and sp3d 2 Hybridization
31.8K
Van der Waals Interactions01:24

Van der Waals Interactions

63.5K
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.
63.5K
Van der Waals Equation01:10

Van der Waals Equation

3.9K
The ideal gas law is an approximation that works well at high temperatures and low pressures. The van der Waals equation of state (named after the Dutch physicist Johannes van der Waals, 1837−1923) improves it by considering two factors.
First, the attractive forces between molecules, which are stronger at higher densities and reduce the pressure, are considered by adding to the pressure a term equal to the square of the molar density multiplied by a positive coefficient a. Second, the volume...
3.9K
Calculations of Electric Potential II01:27

Calculations of Electric Potential II

1.6K
An electric dipole is a system of two equal but opposite charges, separated by a fixed distance. This system is used to model many real-world systems, including atomic and molecular interactions. One of these systems is the water molecule, but only under certain circumstances. These circumstances are met inside a microwave oven, where electric fields with alternating directions make the water molecules change orientation. This vibration is equivalent to heat at the molecular level.
Consider a...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Surface Phase Stability of Fe<sub>2</sub>O<sub>3</sub>(001) in Hydrogen Reducing Environments: A DFT and XPS Analysis.

The journal of physical chemistry letters·2025
Same author

Mechanistic Insights into Acetate Selectivity on Intermetallic CuPd(110) in CO Reduction.

The journal of physical chemistry letters·2025
Same author

Machine Learning-Accelerated First-Principles Molecular Dynamics Explains Anomalous Lattice Thermal Expansion in BaZr<sub>0.78</sub>Y<sub>0.22</sub>O<sub>3-δ</sub>.

The journal of physical chemistry letters·2025
Same author

The Morphology and Interface Structure of Titanium on Graphene.

ACS nano·2025
Same author

Oscillatory redox behavior in oxides: Cyclic surface reconstruction and reactivity modulation via the Mars-van Krevelen mechanism.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Band Gap Narrowing in Lead-Halide Perovskites by Dynamic Defect Self-Doping for Enhanced Light Absorption and Energy Upconversion.

Chemistry of materials : a publication of the American Chemical Society·2025
Same journal

Precursor-Directed Self-Assembly in Hydrothermal Carbon Nitride Nanostructures Revealed by Nano-FTIR.

The journal of physical chemistry letters·2026
Same journal

Correction to "Equation-of-Motion Block-Correlated Coupled Cluster Method for Excited Electronic States of Strongly Correlated Systems".

The journal of physical chemistry letters·2026
Same journal

Rationalizing Stacking-Dependent Charge Injection Dynamics in Radical-Based Organic Light-Emitting Diodes.

The journal of physical chemistry letters·2026
Same journal

Bottom-Up Formation of the Simplest Geminal Thiol─Methanedithiol (CH<sub>2</sub>(SH)<sub>2</sub>)─and the Methyl Hydrodisulfide (H<sub>3</sub>CSSH) Isomer in Interstellar Analogue Ices.

The journal of physical chemistry letters·2026
Same journal

Trion Mediated Sequential Charge Separation in Functionalized CsPbBr<sub>3</sub>/AgInS<sub>2</sub> Hybrid Nanocrystals.

The journal of physical chemistry letters·2026
Same journal

Linking Local Water Electrostatic Potentials to Measured Hydrogen Evolution Onset in Aqueous Electrolytes.

The journal of physical chemistry letters·2026
See all related articles

Related Experiment Video

Updated: Jun 4, 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

5.6K

Overcoming Inaccuracies in Machine Learning Interatomic Potential Implementation for Ionic Vacancy Simulations.

Pandu Wisesa1, Wissam A Saidi1,2

  • 1Department of Mechanical Engineering & Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.

The Journal of Physical Chemistry Letters
|December 18, 2024
PubMed
Summary
This summary is machine-generated.

Deep neural network potentials (DNPs) struggle to accurately calculate vacancy formation energies in ionic materials like MgO. Moment tensor potentials offer a more reliable alternative for these systems.

More Related Videos

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
07:24

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis

Published on: May 10, 2021

5.9K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K

Related Experiment Videos

Last Updated: Jun 4, 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

5.6K
Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
07:24

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis

Published on: May 10, 2021

5.9K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K

Area of Science:

  • Computational Materials Science
  • Materials Informatics
  • Solid State Chemistry

Background:

  • Machine learning interatomic potentials, especially deep neural networks (DNNs), accelerate simulations with high accuracy.
  • DNNs excel in describing pristine ionic systems with multiple oxidation states.

Purpose of the Study:

  • To evaluate the accuracy of deep neural network potentials (DNPs) for calculating vacancy formation energies in the ionic material MgO.
  • To compare the performance of DNPs with moment tensor potentials (MTPs) for ionic systems.

Main Methods:

  • Implementation and testing of deep neural network potentials (DNPs) for MgO.
  • Calculation of vacancy formation energies in MgO using DNPs and MTPs.
  • Analysis of DNP errors in relation to the ionic interaction strength in different oxides (MgO, CuO, AgO).

Main Results:

  • DNPs exhibited a significant error of approximately 3 eV for vacancy formation energies in MgO.
  • Moment tensor potentials (MTPs) accurately predicted vacancy formation energies in MgO.
  • Errors in DNPs correlated with the ionic interaction strength, being larger in MgO than in less ionic Cu2O and Ag2O.

Conclusions:

  • The descriptors used in current deep neural network potentials may be insufficient for accurately modeling vacancies in ionic systems.
  • Moment tensor potentials demonstrate superior accuracy for describing properties, including vacancy formation energies, in ionic oxides like MgO.