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 Plans01:23

Sampling Plans

215
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...
215
Sampling Methods: Overview01:06

Sampling Methods: Overview

386
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...
386
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

290
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...
290
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
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...
12.0K
Random Sampling Method01:09

Random Sampling Method

11.2K
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. Data are the result of sampling from a 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. Among the various sampling methods used by...
11.2K
Sampling Distribution01:12

Sampling Distribution

13.2K
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...
13.2K

You might also read

Related Articles

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

Sort by
Same author

Enzyme Reset: Water-Mediated Tautomerization Restores the Catalytic Asparagine in Protein <i>O</i>-Fucosyltransferase 1.

Journal of chemical information and modeling·2026
Same author

Ceci n'est pas un committor, yet it samples like one: Efficient sampling via approximated committor functions.

The Journal of chemical physics·2026
Same author

Committors without Descriptors.

Journal of chemical theory and computation·2026
Same author

The role of fluctuations in the nucleation process.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

The seeds of the future are in the present: A blind exploration of metastable states.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Theory meets experiment in ammonia decomposition on Li14Cr2N8O: From order to disorder under reaction conditions.

The Journal of chemical physics·2026

Related Experiment Video

Updated: Jul 24, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K

A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar.

Luigi Bonati1, Enrico Trizio1,2, Andrea Rizzi1,3

  • 1Atomistic Simulations, Italian Institute of Technology, 16156 Genova, Italy.

The Journal of Chemical Physics
|July 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces mlcolvar, a Python library for learning collective variables in atomistic simulations. It simplifies enhanced sampling methods by integrating with PLUMED software and employing a multi-task learning framework.

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

628
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

808

Related Experiment Videos

Last Updated: Jul 24, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

628
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

808

Area of Science:

  • Computational chemistry and physics
  • Molecular dynamics simulations
  • Machine learning applications

Background:

  • Identifying collective variables is crucial for understanding and accelerating atomistic simulations.
  • Existing methods for learning collective variables depend on data type, including dimensionality reduction, metastable state classification, and slow mode identification.
  • There is a need for streamlined tools to construct and utilize these variables in enhanced sampling techniques.

Purpose of the Study:

  • To present mlcolvar, a novel Python library designed to simplify the construction and application of collective variables for enhanced sampling in atomistic simulations.
  • To provide a modular framework that facilitates the extension and integration of various machine learning methodologies for collective variable discovery.
  • To introduce a general multi-task learning framework enabling the combination of multiple objective functions and simulation data for improved collective variable identification.

Main Methods:

  • Development of the mlcolvar Python library with a modular architecture.
  • Integration of mlcolvar with the PLUMED software for enhanced sampling.
  • Implementation of a general multi-task learning framework within mlcolvar.
  • Demonstration of library versatility using prototypical simulation scenarios.

Main Results:

  • mlcolvar simplifies the process of learning and applying collective variables for enhanced sampling.
  • The library's modular design supports the development and combination of diverse machine learning approaches.
  • The multi-task learning framework effectively combines information from various sources to enhance collective variable quality.
  • The library is shown to be versatile and applicable to realistic simulation problems.

Conclusions:

  • mlcolvar offers a powerful and flexible tool for researchers in computational chemistry and physics.
  • The library facilitates the advancement of enhanced sampling techniques through improved collective variable identification.
  • The integrated multi-task learning framework represents a significant step forward in leveraging simulation data for molecular modeling.