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

Structural Properties and Dimensions of Lumber01:21

Structural Properties and Dimensions of Lumber

402
Wood's structural properties derive from fibers aligned along the tree's length, contributing significantly to its mechanical strength. Wood exhibits up to twenty times greater tensile strength along these fibers compared to across them, and generally shows better performance under compression than tension. The length of fibers varies, with hardwoods having fibers around one twenty-fifth inch long and softwoods ranging from one-eighth to one-third inch.
The strength characteristics of...
402
Structure and Physical Properties of Alkynes02:37

Structure and Physical Properties of Alkynes

13.3K
Introduction:
In nature, compounds containing both carbon and hydrogen are known as "hydrocarbons". Aliphatic hydrocarbons are compounds whose molecules contain saturated single bonds (i.e., alkanes) or unsaturated double or triple bonds. Alkenes contain carbon–carbon double bonds and have a structural formula CnH2n. Unsaturated hydrocarbons containing carbon–carbon triple bonds are called "alkynes" and are structurally represented by the formula CnH2n-2.
The...
13.3K
Vibrating Concrete01:19

Vibrating Concrete

399
Mechanical vibrators are instrumental in compacting newly poured concrete within formwork and around reinforcements. This process is essential to eliminate trapped air pockets and establish a dense concrete mass. One widely used method is vibrating by internal vibrators, often referred to as a poker vibrator or immersion vibrator. It is rapidly inserted through the full depth of the freshly laid concrete and slightly extends into the layer below it (which remains in a plastic state). Consistent...
399
Machines01:19

Machines

579
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
579
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

18.4K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
18.4K
Machines: Problem Solving II01:30

Machines: Problem Solving II

668
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
668

You might also read

Related Articles

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

Sort by
Same author

Guided Adaptive Diffusion: An Evolutionary Framework for Multimodal Atomistic Structure Prediction.

Journal of chemical information and modeling·2026
Same author

Steering Langevin Dynamics toward Transition States Using Collective-Variable-Free Resampling.

Journal of chemical theory and computation·2026
Same author

How Realistic Are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces.

ACS materials Au·2026
Same author

Accelerating First-Principles Molecular-Dynamics Thermal Conductivity Calculations for Complex Systems.

Journal of chemical theory and computation·2025
Same author

Completing the hierarchy of rotational defects in monolayer MoS<sub>2</sub> through symmetry-aware evolutionary search.

Physical chemistry chemical physics : PCCP·2025
Same author

The density isobar of water: A comparative study of vdW-DF-cx and RPBE-D3.

The Journal of chemical physics·2025

Related Experiment Video

Updated: Feb 3, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

Vibrational Properties of Metastable Polymorph Structures by Machine Learning.

Fleur Legrain1, Ambroise van Roekeghem1, Stefano Curtarolo2,3

  • 1CEA , LITEN , 17 Rue des Martyrs , 38054 Grenoble , France.

Journal of Chemical Information and Modeling
|October 24, 2018
PubMed
Summary
This summary is machine-generated.

Machine learning quickly estimates vibrational properties of solids from atomic positions. This approach accelerates the prediction of material stability at finite temperatures, reducing computational costs.

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K

Related Experiment Videos

Last Updated: Feb 3, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K

Area of Science:

  • Materials Science
  • Computational Physics
  • Solid-State Chemistry

Background:

  • Vibrational properties are crucial for predicting solid-state stability and transport phenomena.
  • High computational costs currently limit the integration of vibrational properties into ab initio materials databases.

Purpose of the Study:

  • To develop a computationally efficient method for estimating vibrational properties of solids.
  • To enable rapid prediction of finite-temperature material stability.

Main Methods:

  • Utilized a random-forest machine learning algorithm trained on atomic equilibrium positions.
  • Focused on predicting interatomic force constants as a key step.

Main Results:

  • Achieved a mean absolute error of 0.17 eV/Ų for interatomic force constants in KZnF₃.
  • Demonstrated accurate estimation of phonon spectral features, heat capacities, and vibrational free energies.
  • Showcased significant computational savings compared to traditional methods.

Conclusions:

  • Machine learning offers a fast and accurate route to estimate vibrational properties.
  • This method can significantly reduce the computational burden for ab initio materials research.
  • Enables broader inclusion of vibrational properties for finite-temperature material property prediction.