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

Sampling Theorem01:15

Sampling Theorem

1.7K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.7K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.3K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
3.3K
Sampling Distribution01:12

Sampling Distribution

17.5K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
17.5K
Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.0K
Sampling Plans01:23

Sampling Plans

1.4K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.4K
Stratified Sampling Method01:16

Stratified Sampling Method

11.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.6K

You might also read

Related Articles

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

Sort by
Same author

Integrating Charge Equilibration with Equivariant Machine-Learning Interatomic Potentials.

Journal of chemical theory and computation·2026
Same author

Bottom-up synthesis of molecular nanodiamond from nanographene.

Nature·2026
Same author

Accelerated Reaction Exploration across Scales: A Hybrid Operando and Modeling Study of Oxidation Kinetics in Monolayer Tungsten Disulfide.

Journal of the American Chemical Society·2026
Same author

Revisiting the phase diagram of hard-sphere dumbbells with nested sampling: Known phases and alternative packing variants.

Physical review. E·2026
Same author

Vibrational infrared and Raman spectra of the methanol molecule with equivariant neural-network property surfaces.

Physical chemistry chemical physics : PCCP·2026
Same author

The role of neutrophils in the pathogenesis of abdominal aortic aneurysms.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]·2026

Related Experiment Video

Updated: Apr 21, 2026

Studying Soft-matter and Biological Systems over a Wide Length-scale from Nanometer and Micrometer Sizes at the Small-angle Neutron Diffractometer KWS-2
11:27

Studying Soft-matter and Biological Systems over a Wide Length-scale from Nanometer and Micrometer Sizes at the Small-angle Neutron Diffractometer KWS-2

Published on: December 8, 2016

13.9K

Nested sampling for materials: the case of hard spheres.

Lívia B Pártay1, Albert P Bartók2, Gábor Csányi2

  • 1University Chemical Laboratory, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 30, 2014
PubMed
Summary

Nested sampling efficiently calculates partition functions for atomistic systems. This study applies it to hard-sphere models, revealing a low barrier for crystallization and minimal entropic impact from jammed states.

More Related Videos

Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles
11:54

Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles

Published on: June 25, 2018

9.9K
Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders
10:10

Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders

Published on: December 4, 2020

1.1K

Related Experiment Videos

Last Updated: Apr 21, 2026

Studying Soft-matter and Biological Systems over a Wide Length-scale from Nanometer and Micrometer Sizes at the Small-angle Neutron Diffractometer KWS-2
11:27

Studying Soft-matter and Biological Systems over a Wide Length-scale from Nanometer and Micrometer Sizes at the Small-angle Neutron Diffractometer KWS-2

Published on: December 8, 2016

13.9K
Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles
11:54

Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles

Published on: June 25, 2018

9.9K
Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders
10:10

Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders

Published on: December 4, 2020

1.1K

Area of Science:

  • Computational physics
  • Statistical mechanics
  • Materials science

Background:

  • Atomistic systems require efficient partition function calculation.
  • Condensed phase systems with periodic boundary conditions present computational challenges.
  • Understanding phase transitions and jammed states is crucial in materials science.

Purpose of the Study:

  • To demonstrate the applicability of nested sampling to condensed phase systems.
  • To calculate the partition function, compressibility, and free energy of the 3D hard-sphere model.
  • To analyze the phase transition to crystallinity and the role of jammed states.

Main Methods:

  • Utilized the nested sampling algorithm for partition function calculation.
  • Studied the three-dimensional hard-sphere model with periodic boundary conditions.
  • Calculated compressibility and free energy as functions of packing fraction and local order.

Main Results:

  • The partition function was efficiently calculated.
  • The transition to crystallinity was found to have a very small energy barrier.
  • The entropic contribution of jammed states to free energy is negligible above the phase transition.
  • Quantified the schematic phase diagram and estimated the region of jammed states.
  • Maximally random jammed configurations were found to be surprisingly disordered.

Conclusions:

  • Nested sampling is a viable method for studying condensed phase systems.
  • The hard-sphere model exhibits a low-barrier transition to crystallinity.
  • Jammed states have a negligible entropic impact on free energy above the phase transition.
  • The study provides quantitative insights into the phase diagram and jammed states.