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

Types of Semiconductors01:20

Types of Semiconductors

687
Intrinsic semiconductors are highly pure materials with no impurities. At absolute zero, these semiconductors behave as perfect insulators because all the valence electrons are bound, and the conduction band is empty, disallowing electrical conduction. The Fermi level is a concept used to describe the probability of occupancy of energy levels by electrons at thermal equilibrium. In intrinsic semiconductors, the Fermi level is positioned at the midpoint of the energy gap at absolute zero. When...
687
Network Covalent Solids02:18

Network Covalent Solids

13.6K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
13.6K

You might also read

Related Articles

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

Sort by
Same author

Prognostic values of red blood cell distribution width, platelet count, and red cell distribution width-to-platelet ratio for severe burn injury.

Scientific reports·2017
Same author

Efficient generation of vector beams by calibrating the phase response of a spatial light modulator.

Applied optics·2017
Same author

Thermally stable multi-color phosphor-in-glass bonded on flip-chip UV-LEDs for chromaticity-tunable WLEDs.

Applied optics·2017
Same author

[Rapid screening of 44 pesticide residues in ginger and scallion by ultra performance liquid chromatography coupled with quadrupole-time of flight mass spectrometry].

Se pu = Chinese journal of chromatography·2017
Same author

Inhibition of early T cell cytokine production by arsenic trioxide occurs independently of Nrf2.

PloS one·2017
Same author

Clinical presentation and surgical treatment of primary pulmonary artery sarcoma.

Interactive cardiovascular and thoracic surgery·2017

Related Experiment Video

Updated: Aug 1, 2025

Silicon Nanowires and Optical Stimulation for Investigations of Intra- and Intercellular Electrical Coupling
08:58

Silicon Nanowires and Optical Stimulation for Investigations of Intra- and Intercellular Electrical Coupling

Published on: January 28, 2021

4.5K

High-Accuracy Neural Network Interatomic Potential for Silicon Nitride.

Hui Xu1, Zeyuan Li2, Zhaofu Zhang1

  • 1The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China.

Nanomaterials (Basel, Switzerland)
|April 28, 2023
PubMed
Summary

Machine learning, specifically Deep Potential Molecular Dynamics (DEEPMD), created a neural network potential (NNP) for silicon nitride (SiNx). Si3N4 exhibited superior mechanical strength due to higher coordination numbers and radial distribution functions.

Keywords:
amorphous silicon nitridedeep potentialdensity functional theorymachine learningmolecular dynamics

More Related Videos

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
13:56

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

7.7K
All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
11:33

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics

Published on: January 19, 2018

9.7K

Related Experiment Videos

Last Updated: Aug 1, 2025

Silicon Nanowires and Optical Stimulation for Investigations of Intra- and Intercellular Electrical Coupling
08:58

Silicon Nanowires and Optical Stimulation for Investigations of Intra- and Intercellular Electrical Coupling

Published on: January 28, 2021

4.5K
Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
13:56

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

7.7K
All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
11:33

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics

Published on: January 19, 2018

9.7K

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Amorphous silicon nitride (SiNx) is a ceramic material with valuable industrial applications due to its electrical insulation, abrasion resistance, and mechanical strength.
  • Developing accurate interatomic potentials is crucial for simulating material properties using molecular dynamics.
  • Deep Potential Molecular Dynamics (DEEPMD) is a powerful machine learning method for creating such potentials.

Purpose of the Study:

  • To develop and validate a neural network potential (NNP) for amorphous silicon nitride (SiNx) using DEEPMD.
  • To investigate the influence of composition on the mechanical properties of SiNx.
  • To establish structure-property relationships at the atomic level.

Main Methods:

  • Utilized Deep Potential Molecular Dynamics (DEEPMD) to construct a neural network potential (NNP) for SiNx.
  • Performed molecular dynamics simulations coupled with the NNP to conduct tensile tests on SiNx models with varying compositions.
  • Analyzed coordination numbers (CN) and radial distribution functions (RDF) to correlate microstructural features with macroscopic mechanical properties.

Main Results:

  • The developed NNP was confirmed to be applicable for simulating SiNx.
  • Si3N4 demonstrated the highest elastic modulus (E) and yield stress (σs) among the simulated compositions.
  • Both coordination numbers (CN) and radial distribution functions (RDF) decreased with increasing nitrogen content (increasing x).
  • A decrease in E and σs was observed as the proportion of silicon increased in SiNx.

Conclusions:

  • The ratio of nitrogen to silicon in SiNx significantly influences its microstructural characteristics (CN, RDF).
  • This nitrogen-to-silicon ratio effectively predicts the macroscopic mechanical properties (E, σs) of SiNx.
  • The study highlights the potential of ML-driven interatomic potentials for understanding and designing advanced ceramic materials.