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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

288
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...
288
Sampling Plans01:23

Sampling Plans

169
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...
169
Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

190
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...
190
Sampling Distribution01:12

Sampling Distribution

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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...
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Contaminants and Errors01:16

Contaminants and Errors

85
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
85

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Overcoming Sampling Issues and Improving Computational Efficiency in Collective-Variable-Based Enhanced-Sampling

Haohao Fu1,2, Mengchen Zhou1,2, Christophe Chipot3,4,5

  • 1Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China.

The Journal of Physical Chemistry. B
|September 25, 2024
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Summary
This summary is machine-generated.

This tutorial introduces advanced simulation methods to enhance sampling efficiency in molecular dynamics. It helps users overcome computational challenges in enhanced-sampling simulations using collective variables.

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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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Area of Science:

  • Computational Chemistry
  • Molecular Dynamics Simulations

Background:

  • Enhanced-sampling simulations using collective variables (CVs) often face challenges with sampling efficiency and computational cost.
  • Accurate characterization of free-energy landscapes requires robust simulation methodologies.

Purpose of the Study:

  • To provide a tutorial for overcoming sampling challenges and improving computational efficiency in CV-based enhanced-sampling simulations.
  • To introduce and demonstrate the utility of well-tempered metadynamics-extended adaptive biasing force (WTM-eABF) integrated with Gaussian accelerated molecular dynamics (GaMD).

Main Methods:

  • Introduction of WTM-eABF combined with GaMD for enhanced molecular dynamics sampling.
  • Application of a method for identifying least-free-energy pathways (LFEP) and multiple concurrent pathways on high-dimensional free-energy surfaces.
  • Utilizing trialanine and chignolin conformational equilibria in aqueous solution as test cases.

Main Results:

  • Demonstration of improved sampling efficiency in molecular dynamics simulations.
  • Successful identification of pathways on high-dimensional free-energy surfaces.
  • Validation of WTM-eABF and GaMD integration for complex molecular systems.

Conclusions:

  • The presented techniques effectively address sampling challenges in enhanced-sampling simulations.
  • The tutorial provides practical guidance for researchers using molecular dynamics and collective variables.
  • The methods are applicable across various molecular dynamics engines supporting the Colvars module.