Jove
Visualize
Contact Us

Related Concept Videos

Hydrogen Bonds01:04

Hydrogen Bonds

8.0K
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...
8.0K
Molecular Orbital Theory II03:51

Molecular Orbital Theory II

19.0K
Molecular Orbital Energy Diagrams
19.0K
Molecular Geometry and Dipole Moments02:36

Molecular Geometry and Dipole Moments

12.7K
The VSEPR theory can be used to determine the electron pair geometries and molecular structures as follows:
12.7K
Molecular Models02:00

Molecular Models

38.0K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.1K
VSEPR Theory for Determination of Electron Pair Geometries
34.1K
MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

10.3K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
10.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Unlocking radical reactivity of cyclic diaryl λ<sup>3</sup>-chloranes through NHC-catalyzed three-component coupling.

Chemical science·2026
Same author

Reactivity of gold and copper acetylide with a secondary borane.

Chemical communications (Cambridge, England)·2025
Same author

Probing the Hybrid σ/π-Holes in sp<sup>2</sup>-Hybridized Chalcogen Bond Donors and their Complexes with Ammonia.

Chemphyschem : a European journal of chemical physics and physical chemistry·2025
Same author

A Crystalline Boravinylidene from an Arrested Hydroboration Reaction.

Journal of the American Chemical Society·2025
Same author

Investigating halogen bonds in substituted graphitic carbon nitride through vibrational spectroscopy.

Physical chemistry chemical physics : PCCP·2025
Same author

Illuminating the Performance of Electron Withdrawing Groups in Halogen Bonding.

Chemphyschem : a European journal of chemical physics and physical chemistry·2024
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 9, 2025

From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding
06:44

From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding

Published on: March 24, 2018

69.0K

Data and Molecular Fingerprint-Driven Machine Learning Approaches to Halogen Bonding.

Daniel P Devore1, Kevin L Shuford1

  • 1Department of Chemistry and Biochemistry, Baylor University, One Bear Place #97348, Waco, Texas 76798-7348, United States.

Journal of Chemical Information and Modeling
|October 29, 2024
PubMed
Summary

Machine learning models predict halogen bond (XB) donor properties and strengths efficiently. This approach offers a faster alternative to expensive ab initio calculations for medicinal chemistry and materials science applications.

More Related Videos

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
Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

4.9K

Related Experiment Videos

Last Updated: Jun 9, 2025

From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding
06:44

From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding

Published on: March 24, 2018

69.0K
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
Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

4.9K

Area of Science:

  • Computational Chemistry
  • Machine Learning in Chemistry
  • Supramolecular Chemistry

Background:

  • Predicting halogen bond (XB) strength and donor properties is crucial for medicinal chemistry and materials science.
  • Current methods like ab initio calculations are computationally expensive.
  • There is a growing need for fast, accurate, and efficient prediction tools.

Purpose of the Study:

  • To develop and apply machine learning models for predicting halogen bond properties.
  • To classify XB donors and complexes based on their principal halogen atom.
  • To predict the electrostatic potential (Vs,max) and interaction strength of XB complexes.

Main Methods:

  • Utilized three machine learning models.
  • Employed molecular fingerprint and data-based analysis.
  • Compared predictions with density functional theory (DFT) calculations.

Main Results:

  • Fingerprint analysis yielded root-mean-square errors of ~7.5 and ~5.5 kcal mol⁻¹ for Vs,max prediction in halobenzene and haloethynylbenzene systems.
  • Binding energy predictions for XB donors and ammonia acceptors were within 1 kcal mol⁻¹ of DFT-calculated energies.
  • Precalculated DFT data led to more accurate predictions than fingerprint analysis.

Conclusions:

  • Machine learning models provide a viable and efficient method for predicting halogen bond properties.
  • The developed models show promise for accelerating research in medicinal chemistry and materials science.
  • Data-driven approaches offer a valuable complement to traditional computational chemistry methods.