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

Ladder Diagrams: Redox Equilibria01:30

Ladder Diagrams: Redox Equilibria

537
Ladder diagrams are useful tools for understanding redox equilibrium reactions, especially the effects of concentration changes on the electrochemical potential of the reaction. The vertical axis in the redox ladder diagrams represents the electrochemical potential, E. The area of predominance is demarcated using the Nernst equation.
Consider the Fe3+/Fe2+ half-reaction, which has a standard-state potential of +0.771 V. At potentials more positive than +0.771 V, Fe3+ predominates, whereas Fe2+...
537
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

36.3K
VSEPR Theory for Determination of Electron Pair Geometries
36.3K
Oxidation Numbers03:14

Oxidation Numbers

38.2K
In redox reactions, the transfer of electrons occurs between reacting species. Electron transfer is described by a hypothetical number called the oxidation number (or oxidation state). It represents the effective charge of an atom or element, which is assigned using a set of rules.
38.2K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.7K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.7K
Properties of Transition Metals02:58

Properties of Transition Metals

27.4K
Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
27.4K
Standard Electrode Potentials03:02

Standard Electrode Potentials

45.1K
On comparing the reactivity of silver and lead, it is observed that the two ionic species, Ag+ (aq) and Pb2+ (aq), show a difference in their redox reactivity towards copper: the silver ion undergoes spontaneous reduction, while the lead ion does not. This relative redox activity can be easily quantified in electrochemical cells by a property called cell potential. This property is commonly known as cell voltage in electrochemistry, and it is a measure of the energy which accompanies the charge...
45.1K

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

Proton beam therapy in nonmetastatic rhabdomyosarcoma: Outcome, prognostic factors and the effect of timing of radiation therapy.

International journal of radiation oncology, biology, physics·2026
Same author

Post-Vaccination COVID-19 Infection among Health Care Workers: Description, Determinants, and Event-History Analysis.

Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine·2026
Same author

Surveillance Outcomes in an Australian Cohort Undergoing Piecemeal Polypectomy for Conventional Adenomas and Serrated Lesions.

Digestive diseases and sciences·2026
Same author

Comment on "Industry guidance on container closure integrity strategies for difficult-to-test parenteral products (DTPs)".

Journal of pharmaceutical sciences·2026
Same author

Advance sensing of high energy explosive: A DFT-Based study of C<sub>5</sub>N framework performance.

Journal of molecular graphics & modelling·2026
Same author

Letter to the editor: Urinary incontinence is common among people attending pulmonary rehabilitation, yet pulmonary rehabilitation has a small effect on urinary symptoms: A multicenter prospective cohort study.

Pulmonology·2026
Same journal

tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal Complexes from SMILES.

Journal of chemical information and modeling·2026
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
Same journal

Derisking Affinity Optimization for Macrocycles and Cyclic Peptides: High-Precision Free Energy Simulations across Five Diverse Targets.

Journal of chemical information and modeling·2026
Same journal

An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.

Journal of chemical information and modeling·2026
Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026
查看所有相关文章

相关实验视频

Updated: Sep 20, 2025

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

94

使用DFT驱动的机器学习预测氧化潜力.

Shweta Sharma1, Natan Kaminsky2, Kira Radinsky2

  • 1Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa 32000, Israel.

Journal of chemical information and modeling
|May 28, 2025
PubMed
概括
此摘要是机器生成的。

我们介绍OxPot,一个包含超过15,000个有机分子的数据集,用于预测氧化潜力 (Eox). 最高占用分子轨道能量 (EHOMO) 与Eox有很强的相关性,使精确的机器学习预测成为可能.

更多相关视频

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
06:53

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks

Published on: June 9, 2023

2.1K
Original Experimental Approach for Assessing Transport Fuel Stability
09:48

Original Experimental Approach for Assessing Transport Fuel Stability

Published on: October 21, 2016

9.4K

相关实验视频

Last Updated: Sep 20, 2025

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

94
Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
06:53

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks

Published on: June 9, 2023

2.1K
Original Experimental Approach for Assessing Transport Fuel Stability
09:48

Original Experimental Approach for Assessing Transport Fuel Stability

Published on: October 21, 2016

9.4K

科学领域:

  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 预测氧化潜力 (Eox) 对于化学研究至关重要.
  • 现有的方法可能缺乏准确性或可扩展性.
  • 存在对强大,机器学习准备的数据集的需求.

研究的目的:

  • 介绍OxPot,这是一个用于Eox预测的大型,开放式访问数据集.
  • 在EHOMO和Eox之间建立可靠的相关性.
  • 促进Eox.机器学习模型的开发.

主要方法:

  • 使用密度函数理论 (DFT) 使用PBE0/cc-pVDZ进行EHOMO计算.
  • 编制了超过15,000种多样化的有机分子 (OxPot) 的数据集.
  • 进行了相关性分析和机器学习算法测试.

主要成果:

  • 在EHOMO和实验Eox.之间取得了强烈的近线性相关性 (R2=0.977).
  • 报告了0.064.064的低平方根平均误差 (RMSE).
  • 通过特征重要性分析确定了影响Eox预测的关键分子描述因素.

结论:

  • OxPot 是一个有价值的, ML 准备的资源,用于加速 Eox 预测.
  • 建立的相关性为准确的预测模型提供了基础.
  • 计算效率允许快速选新的分子.