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

Electric Field01:16

Electric Field

13.6K
Consider two point charges, each exerting Coulomb force on the other. It is possible to describe the Coulomb interaction via an intermediate step by defining a new physical quantity called the electric field.
In the new picture, imagine that the first charge sets up an electric field independent of all other charges in the universe. When another charge comes in its vicinity, the second charge experiences an electric force depending on the electric field at that point. The source charge does not...
13.6K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

414
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...
414
Electric Potential Energy of Two Point Charges01:12

Electric Potential Energy of Two Point Charges

7.7K
The electric potential energy of a test charge in a uniform eclectic field can be generalized to any electric field produced by static charge distribution. Consider a positive test charge in an electric field produced by another static positive charge. If the test charge is moved away from the static charge, then the electric field does the positive work on the test charge, and the electric potential energy of the test charge decreases as it moves away from the static charge. Here the electric...
7.7K
Electric Field of a Non Uniformly Charged Sphere01:22

Electric Field of a Non Uniformly Charged Sphere

2.5K
Gauss's law states that the electric flux through any closed surface equals the net charge enclosed within the surface. This law is beneficial for determining the expressions for the electric field for a particular charge distribution if the electric flux is known.
Consider a non-uniformly charged sphere, for which the density of charge depends only on the distance from a point in space and not on the direction. Such a sphere has a spherically symmetrical charge distribution. Here, the electric...
2.5K
Electric Field of Two Equal and Opposite Charges01:30

Electric Field of Two Equal and Opposite Charges

7.6K
Atoms generally contain the same number of positively and negatively charged particles, protons, and electrons. Hence, they are electrically neutral. However, the centers of the positive and negative charges do not always coincide. In such a scenario, the electric field of an atom may not be zero.
A separation of the positive and negative charges can lead to a weak, remnant effect of the positive and negative charges. The expectation is that the more the distance between the positive and...
7.6K
Coulomb's Law and The Principle of Superposition01:15

Coulomb's Law and The Principle of Superposition

12.1K
Coulomb's Law describes the force experienced by two point charges under each other's presence. But what if there are more than two charges? For example, if there is a third charge, does it experience a force that is a simple combination of the individual forces due to the first two charges? Can it be described mathematically?
The Principle of Superposition answers the question. Yes, Coulomb's Law applies to each pair of charges, and the net force on each charge is the vector sum of...
12.1K

You might also read

Related Articles

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

Sort by
Same author

Annulative endoperoxidation of tetronates.

Organic & biomolecular chemistry·2025
Same author

Comparative analysis of bending moduli in one-component membranes via coarse-grained molecular dynamics simulations.

Biophysical journal·2025
Same author

Comprehensive molecular profiling in MOTION study.

Cancer genetics·2025
Same author

Structural and kinetic characterization of DUSP5 with a Di-phosphorylated tripeptide substrate from the ERK activation loop.

Frontiers in chemical biology·2025
Same author

A Co(III)-peroxo-arylboronate complex formed by nucleophilic reaction of a Co(III)-peroxo species.

Journal of inorganic biochemistry·2024
Same author

Direct Activation of the C(sp<sup>3</sup>)-NH<sub>2</sub> Bond of Primary Aliphatic Alkylamines by a High-Valent Co<sup>III,IV</sup><sub>2</sub>(μ-O)<sub>2</sub> Diamond Core Complex.

Journal of the American Chemical Society·2023

Related Experiment Video

Updated: Apr 17, 2026

Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

4.1K

Genetic algorithm optimization of point charges in force field development: challenges and insights.

Maxim V Ivanov1, Marat R Talipov, Qadir K Timerghazin

  • 1Department of Chemistry, Marquette University , P.O. Box 1881, Milwaukee, Wisconsin 53201-1881, United States.

The Journal of Physical Chemistry. A
|February 5, 2015
PubMed
Summary

Genetic algorithms (GAs) can optimize molecular force fields, but struggle with fitting atomic point charges due to the "buried atom effect." Improving GA performance involves using Hessian or covariance matrix eigenvectors for charge optimization.

More Related Videos

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

802
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

8.8K

Related Experiment Videos

Last Updated: Apr 17, 2026

Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

4.1K
Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

802
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

8.8K

Area of Science:

  • Computational Chemistry
  • Molecular Modeling
  • Biophysics

Background:

  • Genetic algorithms (GAs) are effective for optimizing molecular force field parameters.
  • Fitting nonbonded interaction parameters, including point charges, using GAs is underexplored.
  • Least-squares fitting of atomic point charges to molecular electrostatic potentials (MEPs) presents challenges, particularly the "buried atom effect."

Purpose of the Study:

  • To investigate the performance of genetic algorithms (GAs) for least-squares molecular electrostatic potential (MEP) point charge fitting.
  • To analyze the "buried atom effect" in GA optimizations of point charges.
  • To explore strategies for improving GA performance in force field parameterization.

Main Methods:

  • Application of genetic algorithms (GAs) for least-squares MEP point charge fitting.
  • Analysis of GA solutions using Hessian and covariance matrix eigenvectors.
  • Examination of convergence behavior with respect to different coordinate systems.

Main Results:

  • GA optimizations for MEP point charge fitting exhibit a magnified "buried atom effect," yielding scattered yet correlated solutions.
  • GA convergence is rapid for high-curvature coordinates (related to multipole expansion) but slow for low-curvature coordinates (involving buried atoms).
  • Performance of evolutionary techniques significantly improves when incorporating Hessian or covariance matrix eigenvectors.

Conclusions:

  • The "buried atom effect" poses challenges for GA-based point charge fitting in molecular force fields.
  • Utilizing Hessian or covariance matrix eigenvectors offers a promising approach to enhance evolutionary optimization of fixed-charge biomolecular force fields.