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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

42
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...
42
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

29
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
29
Nuclear Transmutation03:20

Nuclear Transmutation

17.4K
Nuclear transmutation is the conversion of one nuclide into another. It can occur by the radioactive decay of a nucleus, or the reaction of a nucleus with another particle. The first manmade nucleus was produced in Ernest Rutherford’s laboratory in 1919 by a transmutation reaction, the bombardment of one type of nuclei with other nuclei or with neutrons. Rutherford bombarded nitrogen-14 atoms with high-speed α particles from a natural radioactive isotope of radium and observed...
17.4K
In-vitro Mutagenesis01:16

In-vitro Mutagenesis

13.8K
To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
13.8K

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

Cyclic Voltammetry on Rutile IrO<sub>2</sub>(110): Effects and Origin of Lateral Interactions.

The journal of physical chemistry letters·2026
Same author

Local environment neighbor sensitivity analysis: visualization of cation effect at liquid-solid interface.

Chemical communications (Cambridge, England)·2026
Same author

Local diffusion analysis using square displacement averaged in subspace.

The Journal of chemical physics·2026
Same author

Structural Factors of Platinum-Supported Carbon Influencing Ionomer Adsorption in Fuel Cell Catalyst Inks.

ACS applied materials & interfaces·2026
Same author

Erratum: "Machine learning surrogate models for particle insertions and element substitutions" [J. Chem. Phys. 161, 194110 (2024)].

The Journal of chemical physics·2025
Same author

Machine Learning Force Fields in Electrochemistry: From Fundamentals to Applications.

ACS nano·2025

相关实验视频

Updated: Jun 7, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K

机器学习替代模型用于粒子插入和元素替代.

Ryosuke Jinnouchi1

  • 1Toyota Central R&D Labs., Inc., 41-1 Yokomichi, Nagakute, Aichi 480-1192, Japan.

The Journal of chemical physics
|November 19, 2024
PubMed
概括

两种新的机器学习方法可以准确计算液体中原子和分子的化学潜力. 这些方法,粒子插入和元素替代,提供可复制和精确的结果,经实验数据验证.

科学领域:

  • 计算化学计算化学
  • 物理化学 物理化学
  • 机器学习在科学中的应用

背景情况:

  • 精确计算化学潜能对于理解化学反应和材料特性至关重要.
  • 传统的方法在计算上可能昂贵,对复杂的系统来说具有挑战性.

研究的目的:

  • 开发和比较两种机器学习辅助的热力学集成方案,用于计算化学潜力.
  • 验证这些新方法的准确性和可重复性.

主要方法:

  • 开发了两种不同的热力学集成方案:粒子插入和组合粒子插入-元素替换.
  • 在第一原则数据集上训练的机器学习潜力.
  • 通过将潜能整合回第一原则来纠正机器学习模型错误.

主要成果:

  • 两种方法都在统计错误范围内为水中的质子,金属离子和化物离子产生了相同的真实潜力.
  • 计算的真实潜力和溶解结构与实验和模拟数据有很好的一致性.
  • 证明了计算的真实潜力的可重现性.

结论:

  • 机器学习替代模型为确定原子和分子化学潜力提供了精确和可重复的方法.

更多相关视频

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.6K
Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

8.5K

相关实验视频

Last Updated: Jun 7, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.6K
Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

8.5K
  • 开发的方法,粒子插入和元素替代,为化学潜力计算提供可靠的途径.
  • 这些发现推动了机器学习在计算化学中的应用.