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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

648
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...
648
Cluster Sampling Method01:20

Cluster Sampling Method

13.1K
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...
13.1K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

554
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...
554
Sampling Plans01:23

Sampling Plans

332
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...
332
Systematic Sampling Method01:17

Systematic Sampling Method

11.4K
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.
Systematic sampling is one of the simplest methods...
11.4K
Stratified Sampling Method01:16

Stratified Sampling Method

13.2K
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. 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...
13.2K

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Related Experiment Video

Updated: Oct 15, 2025

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

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Collective variable-based enhanced sampling and machine learning.

Ming Chen1

  • 1Department of Chemistry, Purdue University, West Lafayette, IN 47907 USA.

The European Physical Journal. B
|October 26, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning enhances collective variable-based enhanced sampling for complex systems. This review explores integrating AI to improve free energy surface accuracy and addresses future challenges in sampling and kinetic information generation.

Related Experiment Videos

Last Updated: Oct 15, 2025

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

1.3K

Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Machine Learning

Background:

  • Collective variable-based enhanced sampling is crucial for studying thermodynamic properties of complex systems.
  • The efficiency and accuracy of these methods depend on selecting appropriate collective variables and generating precise free energy surfaces.
  • Machine learning (ML) techniques have emerged as powerful tools to improve both aspects of enhanced sampling.

Purpose of the Study:

  • To review recent advancements in integrating machine learning with collective variable-based enhanced sampling.
  • To discuss current challenges and identify future research directions in this interdisciplinary field.

Main Methods:

  • Review of literature integrating ML with enhanced sampling techniques.
  • Analysis of ML applications in constructing collective variables.
  • Examination of ML's role in generating accurate free energy surfaces.

Main Results:

  • ML has shown significant success in improving the quality of collective variables and the accuracy of free energy surfaces.
  • Integration of ML offers new avenues for overcoming limitations in traditional enhanced sampling methods.
  • Several challenges remain, including generating kinetic information and exploring high-dimensional free energy landscapes.

Conclusions:

  • The synergy between ML and enhanced sampling holds great promise for advancing computational studies of complex systems.
  • Future research should focus on addressing challenges in kinetic analysis, high-dimensional sampling, and all-atom configuration sampling.
  • Continued development in ML integration will be key to unlocking deeper insights into molecular thermodynamics and kinetics.