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

Accelerating Fluids01:17

Accelerating Fluids

2.0K
When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
2.0K
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

771
Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
771
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

387
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
387
Surface Tension of Fluid01:22

Surface Tension of Fluid

1.1K
Surface tension is a fundamental property of fluids, occurring at the boundary between a liquid and a gas or between two immiscible liquids. This phenomenon arises from the cohesive forces between molecules at the fluid's surface, creating an effect similar to a stretched elastic membrane. Inside each fluid, molecules are equally attracted in all directions by neighboring molecules, but surface molecules experience a net inward force, resulting in surface tension.
Surface tension varies...
1.1K
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

339
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures enhance...
339
Capillarity in Fluid01:19

Capillarity in Fluid

719
Capillarity describes the movement of liquid in small spaces without external forces acting on it. The capillarity is driven by surface tension and adhesive interactions between the liquid and surrounding solid surfaces. This effect is often seen in narrow tubes, porous materials, and fine particles.
Surface tension is crucial to capillarity. It results from cohesive forces between liquid molecules at the liquid-air boundary, forming a skin that resists external forces. When the capillary tube...
719

You might also read

Related Articles

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

Sort by
Same author

Microwave-assisted thermal profiling of blood: a potential biomarker for differentiating cancer and non-cancer states.

Journal of medical engineering & technology·2026
Same author

Pathway Controlled Phase Separation of Minimal Building Blocks Utilizing a Dissociative Chemical Transformation.

Angewandte Chemie (International ed. in English)·2026
Same author

High-Throughput Screening of Isomeric Reaction Products by Droplet Microfluidics Coupled to Cyclic Ion Mobility-Mass Spectrometry.

Analytical chemistry·2026
Same author

Fragment, Entangle, and Consolidate: Strong Correlation through Bifold Quantum Circuits.

Journal of chemical theory and computation·2026
Same author

Mapping Protein-Protein Interaction Hotspots and Unveiling a Cryptic Allosteric Pocket in PLK1 PBD via Mixed-Solvent Molecular Dynamics.

Chemphyschem : a European journal of chemical physics and physical chemistry·2026
Same author

Excitonic Hamiltonian for Singlet Fission: Beyond a Dimer Model.

Journal of chemical theory and computation·2026

Related Experiment Video

Updated: Dec 24, 2025

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

8.6K

Support Vector Regression-Based Monte Carlo Simulation of Flexible Water Clusters.

Samik Bose1, Suman Chakrabarty2, Debashree Ghosh1

  • 1School of Chemical Sciences, Indian Association for the Cultivation of Science, Kolkata 700032, West Bengal, India.

ACS Omega
|April 14, 2020
PubMed
Summary

This study introduces a machine learning-enhanced many-body expansion (ML-MBE) model for flexible water molecules. The ML-MBE model accurately predicts interaction energies and structural properties, outperforming classical force fields in simulations.

More Related Videos

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

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

Published on: April 12, 2019

13.2K
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.3K

Related Experiment Videos

Last Updated: Dec 24, 2025

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

8.6K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

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

Published on: April 12, 2019

13.2K
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.3K

Area of Science:

  • Computational chemistry
  • Materials science
  • Physical chemistry

Background:

  • Classical force fields offer efficiency but lack accuracy in molecular simulations due to empirical non-bonded interactions.
  • Quantum mechanical (QM) methods provide high accuracy but are computationally prohibitive for large systems.
  • Machine learning (ML) methods are emerging as a solution to bridge the accuracy-efficiency gap in molecular modeling.

Purpose of the Study:

  • To develop a flexible water model using a combined many-body expansion (MBE) and machine learning (ML) scheme.
  • To evaluate the accuracy of the ML-MBE model for predicting interaction energies of flexible water clusters.
  • To assess the performance of the ML-MBE model in simulations of water's structural properties compared to QM and classical methods.

Main Methods:

  • Development of a machine learning-many-body expansion (ML-MBE) scheme for flexible water molecules.
  • Validation of the ML-MBE model against parent quantum mechanical (QM) methods for interaction energy prediction.
  • Execution of machine learning-based Monte Carlo (MLMC) simulations using the developed water model.
  • Comparison of structural properties (radial distribution functions, tetrahedral order parameters, hydrogen bonds) from MLMC, ab initio molecular dynamics (AIMD), and TIP3P classical force field.

Main Results:

  • The ML-MBE scheme achieved an interaction energy prediction error of less than 1% compared to QM methods for flexible water decamers.
  • MLMC simulations using the ML-MBE model demonstrated qualitative and quantitative agreement with AIMD for key structural properties.
  • Classical force fields, specifically TIP3P, exhibited significant deviations in structural properties compared to AIMD and MLMC.

Conclusions:

  • The ML-MBE scheme provides an accurate and computationally feasible approach for modeling flexible water systems.
  • ML-based methods, when combined with MBE, offer a promising alternative to traditional QM and classical force fields for molecular simulations.
  • The developed flexible water model accurately captures the structural characteristics of water, outperforming standard classical force fields.