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

Filtration00:53

Filtration

807
Filtration is a physical separation process that involves passing a suspension through a porous medium to separate solids from fluids. During filtration, solids collect on the porous medium while liquids, also collectively known as the filtrate, pass through. The filtration medium is selected based on the filtration purpose, quantity, and nature of the precipitate. The general criteria for a suitable filtering medium are that it is inert, mechanically strong, nonabsorbent toward dissolved...
807
High-Performance Liquid Chromatography: Types of Detectors01:15

High-Performance Liquid Chromatography: Types of Detectors

508
The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
508
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
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

736
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
736

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

Operando-informed precatalyst programming towards reliable high-current-density electrolysis.

Nature materials·2025
Same author

Efficient low temperature Monte Carlo sampling using quantum annealing.

Scientific reports·2023
Same author

Theoretical Prediction of the Sublimation Behavior by Combining Ab Initio Calculations with Statistical Mechanics.

Materials (Basel, Switzerland)·2023
Same author

Quantum annealing for microstructure equilibration with long-range elastic interactions.

Scientific reports·2023
Same author

A Pragmatic Transfer Learning Approach for Oxygen Vacancy Formation Energies in Oxidic Ceramics.

Materials (Basel, Switzerland)·2022
Same author

Modeling Bainitic Transformations during Press Hardening.

Materials (Basel, Switzerland)·2021

相关实验视频

Updated: Jun 14, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

机器学习的实用功能过策略用于化学中小型数据集的机器学习.

Yang Hu1,2, Roland Sandt3,4, Robert Spatschek3,4,5

  • 1Institute of Energy Materials and Devices IMD-1, Forschungszentrum Jülich GmbH, 52428, Jülich, Germany. y.hu@fz-juelich.de.

Scientific reports
|September 6, 2024
PubMed
概括

本研究介绍了化学和材料科学中机器学习的特征过策略,通过优化特征选择,即使使用小数据集,也可以进行可靠的预测.

更多相关视频

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.9K
Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

12.4K

相关实验视频

Last Updated: Jun 14, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.9K
Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

12.4K

科学领域:

  • 化学 化学 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:

  • 化学和材料科学中的机器学习应用经常因数据集大小小而面临挑战.
  • 有效的模型设计和特征选择对于在数据有限的场景中可靠的预测至关重要.

研究的目的:

  • 提出一个实用和高效的功能过策略来选择最佳的输入功能.
  • 为了证明该策略在预测吸附能量和升华度方面的有效性.

主要方法:

  • 开发并应用一个特征过策略来识别相关的输入特征.
  • 该策略在使用公共数据集预测吸附能量和使用内部数据集预测升华度上进行了测试.
  • 使用并评估了极端梯度增强回归模型.

主要成果:

  • 特性选择将吸附能量预测的输入尺寸从12减少到2,保持精度.
  • 从14种可能性中确定了三种最佳输入配置,用于预测升华度.
  • 最好的机器学习模型实现了与密度函数理论计算与物理可解释性相比的准确性.

结论:

  • 拟议的功能过策略有助于创建可靠的,小型的机器学习培训数据集.
  • 这种方法有助于跨学科的科学家具有有限的AI专业知识或计算资源.
  • 它简化并提高了机器学习模型训练的准确性,减少了时间和改进了功能选择.