Jove
Visualize
Contact Us
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 Concept Videos

Molecular Geometry and Dipole Moments02:36

Molecular Geometry and Dipole Moments

The VSEPR theory can be used to determine the electron pair geometries and molecular structures as follows:
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Electric Dipoles and Dipole Moment01:30

Electric Dipoles and Dipole Moment

Consider two charges of equal magnitude but opposite signs. If they cannot be separated by an external electric field, the system is called a permanent dipole. For example, the water molecule is a dipole, making it a good solvent.
Theoretically, studying electric dipoles leads to understanding why the resultant electric forces around us are weak. Since electric forces are strong, remnant net charges are rare. Hence, the interaction between dipoles helps us understand electrical interactions in...
Induced Electric Dipoles01:28

Induced Electric Dipoles

A permanent electric dipole orients itself along an external electric field. This rotation can be quantified by defining the potential energy because the external torque does work in rotating it. Then, the potential energy is minimum at the parallel configuration and maximum at the antiparallel configuration. While the former is a stable equilibrium, the latter is an unstable equilibrium.
Since the absolute value of potential energy holds no physical meaning, its zero value can be chosen as per...
Molecular Shape and Polarity03:37

Molecular Shape and Polarity

Dipole Moment of a Molecule
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

You might also read

Related Articles

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

Sort by
Same author

Vibrational and Electronic Spectroscopies of Dibenzoterrylene Conformers: Computational Insights.

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
Same author

A Visual Understanding of Circular Dichroism Spectroscopy.

ACS nano·2026
Same author

Chirality transfer from chiral perovskite to molecular dopants via charge transfer states.

Nature communications·2026
Same author

Shadow excited state molecular dynamics with the ΔSCF method.

The Journal of chemical physics·2026
Same author

Towards a theoretical understanding of excitonic properties of phthalocyanine thin films. I. Low-temperature exciton absorption spectra.

Physical chemistry chemical physics : PCCP·2026
Same journal

Mechanism of Dipole Moment Effect on the Schottky Barrier at Janus MoSH/MoSiGeN<sub>4</sub> Metal-Semiconductor Interfaces.

The journal of physical chemistry letters·2026
Same journal

Quantum Trajectory Mean-Field Method Extended for Simulating Laser Field-Induced Nonadiabatic Dynamics.

The journal of physical chemistry letters·2026
Same journal

Self-Sustained Freeze-Sublime Oscillations at a Micrometer Liquid-Vacuum Interface.

The journal of physical chemistry letters·2026
Same journal

<i>Ab Initio</i> Simulation of Femtosecond Time-Resolved Multipulse Spectroscopies Applied to the Heptazine···H<sub>2</sub>O Complex.

The journal of physical chemistry letters·2026
Same journal

From Exciton to Tetraexciton: Final-State Doublets and 1P Addition Energies in a Weakly Confined Perovskite Quantum Dot.

The journal of physical chemistry letters·2026
Same journal

Photoinduced Structural Instability Toward the Superionic Phase Transition in Cu<sub>2</sub>S.

The journal of physical chemistry letters·2026
See all related articles
  1. Home
  3. Enhancing Molecular Dipole Moment Prediction With Multitask Machine Learning.
  1. Home
  3. Enhancing Molecular Dipole Moment Prediction With Multitask Machine Learning.

Related Experiment Video

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Enhancing Molecular Dipole Moment Prediction with Multitask Machine Learning.

William Colglazier1, Nicholas Lubbers2, Sergei Tretiak1,3,4

  • 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.

The Journal of Physical Chemistry Letters
|May 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a machine learning approach that improves molecular dipole moment predictions by training on both dipole magnitudes and Mulliken atomic charges. Incorporating less accurate charge data boosted prediction accuracy by up to 30%.

Related Experiment Videos

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Computational chemistry
  • Machine learning applications in chemistry

Background:

  • Predicting molecular dipole moments is crucial for understanding molecular properties.
  • Traditional methods may lack efficiency or accuracy.
  • Machine learning offers potential for improved predictions.

Purpose of the Study:

  • To develop a multitask machine learning strategy for enhancing molecular dipole moment prediction accuracy.
  • To investigate the utility of incorporating low-quality auxiliary data (Mulliken charges) into the training process.
  • To assess the impact of auxiliary data on learning physically grounded molecular representations.

Main Methods:

  • Implemented a multitask learning framework.
  • Trained a model simultaneously on quantum dipole magnitudes (primary target) and Mulliken atomic charges (auxiliary task).
  • Utilized a weighted loss function to balance the contribution of each task.

    • Main Results:

    • Incorporating Mulliken atomic charges improved dipole prediction accuracy by up to 30%.
    • The multitask approach led to more physically grounded representations of molecular charge distributions.
    • Enhanced accuracy and consistency in predicting dipole magnitudes were observed.

    Conclusions:

    • Multitask learning with auxiliary data, even if quantitatively unreliable, can significantly improve predictive models.
    • Qualitative physical insights from low-quality data enhance machine learning model performance.
    • This strategy offers a pathway to more accurate and robust predictions of molecular properties.