Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Hydrogen Bonds01:04

Hydrogen Bonds

15.3K
A hydrogen bond is formed when a weakly positive hydrogen atom already bonded to one electronegative atom (for example, the oxygen in the water molecule) is attracted to another electronegative atom from another polar molecule, such as water (H2O), hydrogen fluoride (HF), or ammonia (NH3). The huge electronegativity difference between the H atom (2.1) and the atom to which it is bonded (4.0 for an F atom, 3.5 for an O atom, or 3.0 for an N atom), combined with the very small size of an H atom...
15.3K
Hydrogen Bonds00:26

Hydrogen Bonds

135.0K
Hydrogen bonds are weak attractions between atoms that have formed other chemical bonds. One of these atoms is electronegative, like oxygen, and has a partial negative charge. The other is a hydrogen atom that has bonded with another electronegative atom and has a partial positive charge.
Hydrogen Bonds Control the World!
Because hydrogen has very weak electronegativity when it binds with a strongly electronegative atom, such as oxygen or nitrogen, electrons in the bond are unequally shared....
135.0K
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

65.4K
Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
65.4K
IR Spectrum Peak Broadening: Hydrogen Bonding01:23

IR Spectrum Peak Broadening: Hydrogen Bonding

1.9K
The vibrational frequency of a bond is directly proportional to its bond strength. As a result, stronger bonds vibrate at higher frequencies, while weaker bonds vibrate at lower frequencies. The stretching vibration of the strong O–H bond in alcohols and phenols (very dilute solution or gas phase) appears as a sharp peak at 3600–3650 cm−1.
However, the extent of hydrogen bonding influences the observed stretching frequency and band broadening. Intermolecular or intramolecular...
1.9K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.2K
VSEPR Theory for Determination of Electron Pair Geometries
46.2K
Introduction to Chemical Bonds01:01

Introduction to Chemical Bonds

12.8K
Chemical Bonds
The electrons of the outermost energy level determine the energetic stability of the atom and its tendency to form chemical bonds with other atoms. The innermost electron shell has a maximum capacity of two electrons, but the next two electron shells can each have a maximum of eight electrons. This is known as the octet rule, which states that, with the exception of the innermost shell, atoms are most stable energetically when they have eight electrons in their valence shell, the...
12.8K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations.

The Journal of chemical physics·2024
Same author

Immobilization of Bi<sub>2</sub>WO<sub>6</sub> on Polymer Membranes for Photocatalytic Removal of Micropollutants from Water - A Stable and Visible Light Active Alternative.

Global challenges (Hoboken, NJ)·2024
Same author

Efficient Molecular Dynamics Simulations of Deep Eutectic Solvents with First-Principles Accuracy Using Machine Learning Interatomic Potentials.

Journal of chemical theory and computation·2023
Same author

Ion Correlation in Choline Chloride-Urea Deep Eutectic Solvent (Reline) from Polarizable Molecular Dynamics Simulations.

The journal of physical chemistry. B·2022
Same author

Elucidating the Role of Halides and Iron during Radiolysis-Driven Oxidative Etching of Gold Nanocrystals Using Liquid Cell Transmission Electron Microscopy and Pulse Radiolysis.

Journal of the American Chemical Society·2021
Same author

Molecular Features of Reline and Homologous Deep Eutectic Solvents Contributing to Nonideal Mixing Behavior.

The journal of physical chemistry. B·2020

関連する実験動画

Updated: Feb 17, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.6K

機械学習のアプローチによる水素結合の強さの推定

Nahera Samangani1, Stefan Zahn1

  • 1Leibniz Institute of Surface Engineering (IOM), Permoserstraße 15, Leipzig 04318, Germany.

ACS omega
|February 16, 2026
PubMed
まとめ

機械学習モデルは,分子記述子を使用して,水素結合エネルギーを正確に予測します. グラデーションブーストによるサポートベクトル回帰では,3%の誤差が達成され,計算化学アプリケーションの以前の方法の改善となった.

科学分野:

  • 計算化学はコンピュータ化学である.
  • 化学における機械学習

背景:

  • 水素結合エネルギーの正確な予測は,分子相互作用を理解するために重要である.
  • 水素結合エネルギーを計算するための既存の方法は,計算的に高価である可能性があります.

研究 の 目的:

  • 水素結合エネルギーの予測のための機械学習のアプローチを調査する.
  • 水素結合エネルギーに影響を与える重要な分子記述者を特定する.

主な方法:

  • グラデーションの強化と組み合わせたサポートベクトル回帰を活用しました.
  • BLYPまたはB3LYPからdef2-SVPベースセットによる部分請求および債券注文を雇ったLöwdin.
  • マリケンの部分料金とウィーバーグの債券注文について,半実証的なGFN2-xTBアプローチを考察した.

主要な成果:

  • 最高のモデルでは平均絶対誤差3%を達成した.
  • 以前の予測モデルと比較して有意な改善を示した.
  • GFN2-xTBのアプローチでは,平均4%の絶対パーセント誤差が得られました.

結論:

  • 機械学習モデルは,水素結合エネルギーを効果的に予測することができます.

さらに関連する動画

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.9K
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

8.7K

関連する実験動画

Last Updated: Feb 17, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.6K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.9K
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

8.7K
  • Löwdinの部分請求やBLYP/B3LYPの債券注文のような特定の記述者は,高い精度を提供します.
  • GFN2-xTBのアプローチは,機能生成のための実行可能な代替案を提供します.