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相关概念视频

Sampling Methods: Overview01:06

Sampling Methods: Overview

309
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...
309
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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...
11.9K
Sampling Distribution01:12

Sampling Distribution

12.4K
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...
12.4K
Random Sampling Method01:09

Random Sampling Method

11.0K
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...
11.0K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

214
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...
214
Sampling Plans01:23

Sampling Plans

181
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...
181

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相关实验视频

Updated: Jun 26, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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深度学习路径类集体变量用于增强采样分子动态.

Thorben Fröhlking1,2,3, Luigi Bonati4, Valerio Rizzi1,2,3

  • 1School of Pharmaceutical Sciences, University of Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland.

The Journal of chemical physics
|May 15, 2024
PubMed
概括
此摘要是机器生成的。

我们介绍了DeepLNE,这是一种新的路径类集体变量,用于增强采样. 这种方法有效地模仿反应坐标,并加速分子动力学模拟用于自由能量计算.

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

  • 计算化学计算化学
  • 生物物理学的生物物理.
  • 机器学习 机器学习

背景情况:

  • 改进的采样技术对于探索复杂的自由能源景观至关重要.
  • 现有的反应路径集体变量在定义复杂路径方面存在局限性.

研究的目的:

  • 引入一种新的路径类型集体变量,DeepLNE (深部局部非线性嵌入).
  • 解决目前探索反应途径的方法的局限性.

主要方法:

  • DeepLNE受到局部线性嵌入的启发,并接受了反应轨迹的训练.
  • 使用可分化通用自编码器,结合神经网络和k-最近邻居选择.
  • 自动选择邻居搜索指标,并学习路径,没有手动地标选择.

主要成果:

  • 在玩具模型中,DeepLNE非常接近理想的反应坐标 (米勒-布朗潜力,氨二).
  • 加快过渡,并估计在RNA四旋翼分子动力学模拟的折叠自由能量.

结论:

  • DeepLNE提供了一种有效和自动化的方法来定义反应坐标.
  • 在加速分子动力学模拟和对复杂系统的自由能量估计方面表现出实用性.