Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Intermolecular Forces

Intermolecular Forces

Atoms and molecules interact through bonds (or forces): intramolecular and intermolecular. The forces are electrostatic as they arise from interactions (attractive or repulsive) between charged species (permanent, partial, or temporary charges) and exist with varying strengths between ions, polar, nonpolar, and neutral molecules. The different types of intermolecular forces are ion–dipole, dipole–dipole, hydrogen bonds, and dispersion; among these, dipole–dipole, hydrogen...

Molecular Geometry and Dipole Moments

Molecular Geometry and Dipole Moments

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

Molecular Shape and Polarity

Molecular Shape and Polarity

Dipole Moment of a Molecule

Potential Due to a Polarized Object

Potential Due to a Polarized Object

A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...

Molecular Models

Molecular Models

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.

The Fluid Mosaic Model

The Fluid Mosaic Model

The fluid mosaic model was first proposed as a visual representation of research observations. The model comprises the composition and dynamics of membranes and serves as a foundation for future membrane-related studies. The model depicts the structure of the plasma membrane with a variety of components, which include phospholipids, proteins, and carbohydrates. These integral molecules are loosely bound, defining the cell’s border and providing fluidity for optimal function.

You might also read

Related Articles

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

Sort by

Same author

Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding.

Journal of chemical theory and computation·2026

Same author

Energetics of Noncovalent Interactions of Protein-Ligand Complexes for Drug Discovery.

Journal of chemical information and modeling·2026

Same author

Development and application of external electric fields for advanced polarizable force fields.

The Journal of chemical physics·2026

Same author

Sensing the acidity of hydrogen bond networks.

Physical chemistry chemical physics : PCCP·2026

Same author

SmileyLlama: modifying large language models for directed chemical space exploration.

Nature computational science·2026

Same author

Conformational Ensembles of the Disordered 4E-BP2:eIF4E Complex Restrained by smFRET Experiments.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Apr 17, 2026

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

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

Published on: April 26, 2024

Revised Parameters for the AMOEBA Polarizable Atomic Multipole Water Model.

Marie L Laury¹, Lee-Ping Wang², Vijay S Pande²

¹Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63105.

The Journal of Physical Chemistry. B

|February 17, 2015

Summary

The new AMOEBA14 water model offers improved polarizable atomic multipole parameters. This enhanced model accurately predicts liquid water properties across a wide temperature range, outperforming previous versions.

More Related Videos

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

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

Published on: April 12, 2019

Assembly and Characterization of Polyelectrolyte Complex Micelles

Assembly and Characterization of Polyelectrolyte Complex Micelles

Published on: March 2, 2020

Related Experiment Videos

Last Updated: Apr 17, 2026

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

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

Published on: April 26, 2024

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

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

Published on: April 12, 2019

Assembly and Characterization of Polyelectrolyte Complex Micelles

Assembly and Characterization of Polyelectrolyte Complex Micelles

Published on: March 2, 2020

Area of Science:

Computational chemistry
Molecular modeling
Physical chemistry

Background:

Accurate molecular models are crucial for simulating water properties.
Previous AMOEBA models required further refinement for broad applicability.
Polarizable force fields capture essential electronic effects in water.

Purpose of the Study:

To develop an improved AMOEBA polarizable water model (AMOEBA14).
To refine parameters using automated procedures and diverse validation data.
To enhance the prediction of liquid water properties across temperatures.

Main Methods:

Utilized the ForceBalance automated procedure for parameter optimization.
Calibrated against ab initio calculations for water clusters (2-20 molecules).
Validated against experimental liquid phase properties over a broad temperature range.

Main Results:

The AMOEBA14 model accurately predicts the temperature of maximum density.
Qualitative agreement with experimental density curve from 249 to 373 K.
Excellent reproduction of various temperature-dependent properties: second virial coefficient, enthalpy of vaporization, compressibility, thermal expansion, dielectric constant, viscosity, diffusion, and surface tension.

Conclusions:

AMOEBA14 represents a significant advancement over AMOEBA03.
The model demonstrates high fidelity in predicting both cluster and condensed-phase properties.
Recommended for future simulations requiring a polarizable water model due to its accuracy and broad applicability.