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Related Concept Videos

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.
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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to occupy...
Phase Transitions01:21

Phase Transitions

A phase transition is the process in which a substance changes from one state of matter to another, like from a solid to a liquid, liquid to gas, or vice versa, at a specific temperature and under given pressure conditions. This change is spontaneous and is affected by alterations in temperature and pressure. These parameters impact the strength of the forces between molecules (intermolecular forces) in the substance.During a phase transition, both the initial and final phases of the substance...
Sampling Plans01:23

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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: 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...

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Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
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Comparing parallel- and simulated-tempering-enhanced sampling algorithms at phase-transition regimes.

Carlos E Fiore1, M G E da Luz

  • 1Departamento de Física, Universidade Federal do Paraná, CP 19044, 81531-980 Curitiba, PR, Brazil. fiore@fisica.ufpr.br

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 15, 2011
PubMed
Summary
This summary is machine-generated.

Parallel tempering (PT) simulations are more effective than simulated tempering (ST) for studying systems with large energy barriers, such as those near first-order phase transitions. PT shows faster convergence and better phase exploration in these challenging simulations.

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Area of Science:

  • Computational Physics
  • Statistical Mechanics
  • Phase Transitions

Background:

  • Enhanced sampling algorithms like simulated tempering (ST) and parallel tempering (PT) are crucial for simulations encountering large free-energy barriers, common in systems near first-order phase transitions.
  • The application of these methods to phase transition regimes remains an area requiring further exploration.
  • Ergodicity can be challenging to achieve in simulations with significant free-energy barriers.

Purpose of the Study:

  • To conduct a comparative analysis of parallel tempering (PT) and simulated tempering (ST) algorithms.
  • To evaluate the performance of PT versus ST in modeling systems at phase-transition regimes.
  • To investigate the efficiency of both methods for systems with large free-energy barriers.

Main Methods:

  • Comparative simulation study of the Ising and Blume-Emery-Griffiths models.
  • Application of both parallel tempering (PT) and simulated tempering (ST) algorithms.
  • Analysis focused on convergence, phase tunneling frequency, and correlation function decay.

Main Results:

  • Parallel tempering (PT) demonstrated superior performance compared to simulated tempering (ST) across all analyzed metrics.
  • PT exhibited faster convergence towards stationarity and a higher frequency of phase tunneling at coexistence.
  • Decay of time-displaced correlation functions for thermodynamic quantities was more effectively captured by PT.

Conclusions:

  • Parallel tempering (PT) offers significant advantages over simulated tempering (ST) for simulations near first-order phase transitions.
  • PT provides more efficient exploration of phase space and faster convergence in systems with large free-energy barriers.
  • Qualitative arguments support the expectation of PT's superior performance under phase-transition conditions.