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 Continuous Time Signal01:11

Sampling Continuous Time Signal

819
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
819
Sampling Methods: Overview01:06

Sampling Methods: Overview

3.8K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
3.8K
Sampling Theorem01:15

Sampling Theorem

1.5K
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.
1.5K
Sampling Plans01:23

Sampling Plans

1.2K
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...
1.2K
Bandpass Sampling01:17

Bandpass Sampling

608
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
608
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.6K
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...
3.6K

You might also read

Related Articles

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

Sort by
Same author

Edge Reconstruction in a Quantum Spin Hall Insulator.

Physical review letters·2026
Same author

Generalized Suzuki-Chin factorization in bosonic path integral molecular dynamics.

Physical chemistry chemical physics : PCCP·2026
Same author

Friedel Oscillations in Nanoconfined ^{4}He.

Physical review letters·2026
Same author

fix pimd/langevin: An efficient implementation of path integral molecular dynamics in LAMMPS.

The Journal of chemical physics·2026
Same author

Accurate helium-benzene potential: From CCSD(T) to Gaussian process regression.

The Journal of chemical physics·2026
Same author

The Molecular Basis of Growth Control in Guanine Crystals.

Small (Weinheim an der Bergstrasse, Germany)·2026

Related Experiment Video

Updated: Mar 21, 2026

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

8.8K

Combining harmonic sampling with the worm algorithm to improve the efficiency of path integral Monte Carlo.

Sourav Karmakar1,2, Sutirtha Paul3, Adrian Del Maestro3,4

  • 1Tel Aviv University, School of Chemistry, Tel Aviv 6997801, Israel.

Physical Review. E
|March 20, 2026
PubMed
Summary

We introduce harmonic PIMC (H-PIMC) and mixed PIMC (M-PIMC) algorithms to improve quantum condensed phase simulations. These methods enhance sampling efficiency for solids and dense liquids by optimizing the Path Integral Monte Carlo (PIMC) algorithm.

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

5.2K
Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans
11:16

Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans

Published on: May 28, 2020

6.7K

Related Experiment Videos

Last Updated: Mar 21, 2026

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

8.8K
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

5.2K
Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans
11:16

Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans

Published on: May 28, 2020

6.7K

Area of Science:

  • Quantum Many-Body Physics
  • Computational Physics

Background:

  • Path Integral Monte Carlo (PIMC) is crucial for quantum condensed phases.
  • Standard PIMC faces challenges with low acceptance ratios in solids and dense liquids.

Purpose of the Study:

  • To develop improved PIMC algorithms (H-PIMC and M-PIMC) for enhanced efficiency.
  • To address sampling limitations in solids and dense confined liquids.

Main Methods:

  • Developed H-PIMC: exact sampling of harmonic potential contributions, acceptance based on anharmonic part.
  • Developed M-PIMC: restricted harmonic sampling near minima, standard PIMC elsewhere.
  • Combined H-PIMC/M-PIMC with the worm algorithm for indistinguishable particles.

Main Results:

  • H-PIMC significantly improves acceptance ratios (6-16x) and reduces autocorrelation times (7-30x) for weakly to moderately anharmonic systems.
  • H-PIMC achieves faster convergence, reducing required imaginary time slices by 2-3x.
  • M-PIMC optimizes autocorrelation time for strongly anharmonic systems and periodic potentials.

Conclusions:

  • H-PIMC and M-PIMC offer substantial efficiency gains for PIMC simulations.
  • These methods effectively overcome sampling limitations in challenging condensed matter systems.
  • Integration with the worm algorithm extends benefits to systems of indistinguishable particles.