Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Sampling Distribution01:12

Sampling Distribution

12.3K
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...
12.3K
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
Response Surface Methodology01:16

Response Surface Methodology

98
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
98
Cluster Sampling Method01:20

Cluster Sampling Method

11.8K
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...
11.8K
Probability Distributions01:32

Probability Distributions

6.8K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.8K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

LB-PaCS-MD Guided Simulations Reveal Transient Stabilization during TCR-pMHC Dissociation.

Journal of chemical information and modeling·2026
Same author

Beyond Passive Substituents: Tosyl-Directed Self-Templation Enables Selective Pillar[4 + 1]arene Formation and Topology Switching.

Journal of the American Chemical Society·2026
Same author

Computational insights into the pH-dependent behavior of Ipilimumab-CTLA-4.

Physical chemistry chemical physics : PCCP·2026
Same author

2-Mercaptophenylboronic acid: a superior alternative to 2-mercaptoethanol for thioester hydrolysis.

Organic & biomolecular chemistry·2026
Same author

Highly Dispersed Pt-Decorated Oxygen-Vacancy-Rich MOF-Derived SnO<sub>2</sub> Nanostructures on MEMS Hot Plate for ppb-Level Hydrogen Detection.

ACS applied materials & interfaces·2026
Same author

Sequential, Multistep, and Cooperative Helicity Evolution in Supramolecular Polymers of Chlorophyll Rosettes.

Journal of the American Chemical Society·2026

相关实验视频

Updated: Jun 14, 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

1.0K

一个基于辐射分布函数采样的机器学习潜力构造.

Natsuki Watanabe1,2, Yuta Hori1, Hiroki Sugisawa3

  • 1Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan.

Journal of computational chemistry
|September 3, 2024
PubMed
概括

本研究介绍了基于辐射分布函数 (RDF) 的数据采样,以提高机器学习潜力 (MLP). 这种方法通过确保准确的参考数据来增强分子动力学 (MD) 模拟,防止非物理行为.

关键词:
机器学习潜在的机器学习潜力分子集群是分子集群.量子化学计算的量子化学计算辐射分布的功能是辐射分布的功能.培训数据采样 培训数据采样

更多相关视频

Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles
08:39

Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles

Published on: October 16, 2017

12.7K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.8K

相关实验视频

Last Updated: Jun 14, 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

1.0K
Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles
08:39

Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles

Published on: October 16, 2017

12.7K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.8K

科学领域:

  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习

背景情况:

  • 准确的参考数据对于构建可靠的机器学习潜力 (MLP) 至关重要.
  • 训练数据中的局部配置不足可能导致基于MLP的分子动力学 (MLP-MD) 模拟中的非物理行为.

研究的目的:

  • 开发参考数据的飞行采样方法,以增强MLP构建.
  • 提高MLP用于分子动力学模拟的准确性和稳定性,特别是用于水系统.

主要方法:

  • 提出了一种基于辐射分布函数 (RDF) 的新型数据采样技术,用于飞行中的参考数据收集.
  • 通过分析RDF形状,检测并从MLP-MD轨迹中提取异常配置.
  • 将这些结构集成到参考数据集中,以完善MLP.

主要成果:

  • 使用新采样方法的MLP-MD模拟产生了具有物理现实的特征的轨迹,包括精确的RDF形状和角度分布.
  • 精制的MLP表现出强度,准确地模拟了从分子集群数据中的散装水系统.
  • 在没有基于RDF的采样的模拟中观察到的非物理行为得到了有效的缓解.

结论:

  • 基于RDF的数据采样方法是构建准确和强大的MLP的高效策略.
  • 这种方法使得从小分子系统可靠地推断到更大,散装系统,而不需要专门的专业知识.
  • 该技术显著提高了MLP-MD模拟的质量,将结果与初始计算对齐.