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

Atomic Absorption Spectroscopy: Atomization Methods01:25

Atomic Absorption Spectroscopy: Atomization Methods

324
Atomic Absorption Spectroscopy (AAS) atomizes samples through flame atomization or electrothermal atomization. Flame atomization typically involves a nebulizer and spray chamber assembly to combine the sample with a fuel–oxidant mixture, creating a fine aerosol mist that enters a burner. Typically, the fuel and oxidant are combined in an approximately stoichiometric ratio. However, for atoms that are easily oxidized, a fuel-rich mixture may be more advantageous. Only about 5% of the...
324
Atomic Absorption Spectroscopy: Interference01:25

Atomic Absorption Spectroscopy: Interference

551
Interference leads to systematic error in atomic absorption (AA) measurements by enhancing or diminishing the analytical signal or the background. These interferences can be grouped into three main categories: spectral interference, chemical interference, and physical interference.
Spectral interference occurs when signals from other elements or molecules overlap with the analyte signal, falsely elevating or masking the analyte's absorbance. This interference can be corrected using Zeeman,...
551
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

7.9K
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,...
7.9K
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

41.7K
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing...
41.7K

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

Reactive Chemistry at the Unrestricted Coupled Cluster Level: High-Throughput Calculations for Training Machine Learning Potentials.

Journal of chemical theory and computation·2026
Same author

Machine learning-accelerated screening of hydroquinone analogs for proton-coupled electron transfer.

Chemical science·2026
Same author

Electron Alchemy with Machine-Learned Interatomic Potentials: Case Studies of Local Charge in Bond Dissociation Curves.

Journal of chemical theory and computation·2026
Same author

Exploring celecoxib polymorph landscape using AIMNet2 machine learning interatomic potential.

Chemical science·2026
Same author

Enhancing Molecular Dipole Moment Prediction with Multitask Machine Learning.

The journal of physical chemistry letters·2026
Same author

Knowledge distillation of noisy force labels for improved coarse-grained force fields.

The Journal of chemical physics·2026

相关实验视频

Updated: May 17, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K

包括反应化学神经网络中的基于物理的原子化约束.

Shuhao Zhang1, Michael Chigaev2,3, Olexandr Isayev1

  • 1Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

Journal of chemical information and modeling
|April 29, 2025
PubMed
概括

本研究引入了一种新的方法,通过准确计算与孤立原子系统的能量来提高机器学习原子间潜力 (MLIP). 这提高了神经网络模型对各种化学过程的可靠性.

更多相关视频

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

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

Published on: April 12, 2019

12.7K
Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
05:57

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

301

相关实验视频

Last Updated: May 17, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

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

Published on: April 12, 2019

12.7K
Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
05:57

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

301

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.
  • 机器学习是机器学习.

背景情况:

  • 机器学习原子间潜力 (MLIP) 为原子模拟提供了高精度和效率.
  • 目前基于神经网络 (NN) 的MLIP难以准确预测孤立或几乎孤立原子的能量.
  • 这种限制影响了涉及这些物种的反应过程的模拟.

研究的目的:

  • 开发一种数学技术来增强NN MLIPs,以准确预测孤立原子的能量.
  • 为了确保在不同系统配置中对原子化能量 (AE) 的一致预测.
  • 在化学模拟中提高MLIP的整体性能和可靠性.

主要方法:

  • 介绍了一种数学技术来修改现有的以原子为中心的NN架构.
  • 开发了已建立的MLIP模型的AE受限版本:HIP-NN-AE和ANI-AE.
  • 评估了AE预测,键解离能和可扩展性测试上的模型性能.

主要成果:

  • 受到AE限制的模型显示,AE预测准确度显著提高.
  • 新技术确保了能源预测的一致性,特别是对于具有孤立原子的系统.
  • 在不影响现有能力的情况下,在其他任务中观察到性能改进.

结论:

  • 拟议的AE约束方法为处理MLIP中的孤立原子提供了可靠的解决方案.
  • 这种方法提高了神经网络潜力的预测能力和可靠性.
  • 该技术提供了一种可通用的方法来改进各种NN MLIP架构.