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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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...
Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...
Sampling Theorem01:15

Sampling Theorem

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.
Sampling Distribution01:12

Sampling Distribution

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...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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...

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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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Enhanced sampling in the well-tempered ensemble.

M Bonomi1, M Parrinello

  • 1Computational Science, Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, via Buffi 13, CH-6900 Lugano, Switzerland. mbonomi@ethz.ch

Physical Review Letters
|September 28, 2010
PubMed
Summary
This summary is machine-generated.

We introduce the well-tempered ensemble (WTE), a novel simulation method that significantly accelerates phase space exploration. This technique enhances computational efficiency for molecular dynamics and materials science simulations.

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Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Biophysics

Background:

  • Molecular dynamics simulations are crucial for understanding complex systems.
  • Traditional methods can be limited by slow exploration of the phase space.
  • Efficient sampling techniques are needed to overcome these limitations.

Purpose of the Study:

  • To introduce the well-tempered ensemble (WTE) as a novel simulation method.
  • To demonstrate the efficiency of WTE in accelerating phase space exploration.
  • To show WTE's applicability in conjunction with parallel tempering and reweighting methods.

Main Methods:

  • Development and application of the well-tempered ensemble (WTE).
  • Utilizing well-tempered metadynamics with energy as the collective variable.
  • Combining WTE with parallel tempering for enhanced efficiency.
  • On-the-fly computation of unbiased Boltzmann averages using a reweighting method.

Main Results:

  • WTE achieves similar average energy to canonical ensembles but with larger fluctuations.
  • WTE demonstrates extremely fast exploration of phase space.
  • Combining WTE with parallel tempering further increases efficiency.
  • Orders of magnitude acceleration in convergence observed for the 2D Ising model and HIV protease Gō model.

Conclusions:

  • WTE is a highly efficient method for accelerating molecular simulations.
  • The combination of WTE with parallel tempering and reweighting offers significant computational advantages.
  • WTE provides a powerful tool for studying complex systems in chemistry and biophysics.