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

Multimachine Stability01:25

Multimachine Stability

633
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
633
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

3.1K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
3.1K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

3.2K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
3.2K
Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

452
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
452
Kinematic Equations - II01:17

Kinematic Equations - II

15.9K
The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
15.9K
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

747
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
747

You might also read

Related Articles

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

Sort by
Same author

Plasma-assisted CH<sub>4</sub> activation on Cu/CeO<sub>2</sub> catalysts: insights into the effect of catalyst surface and vibrational excitation.

Physical chemistry chemical physics : PCCP·2026
Same author

Driving catalytic carbyne formation within endohedral DWCNTs: the role of Ni <i>vs.</i> Pt.

Nanoscale·2025
Same author

Heterocycle- and Amine-Free Electrochromic and Electrofluorochromic Molecules for Energy-Saving See-Through Smart Windows and Displays.

Chemistry (Weinheim an der Bergstrasse, Germany)·2024
Same author

Capillary Condensation of Water in Graphene Nanocapillaries.

Nano letters·2024
Same author

Reduction-enhanced water flux through layered graphene oxide (GO) membranes stabilized with H<sub>3</sub>O<sup>+</sup> and OH<sup>-</sup> ions.

Physical chemistry chemical physics : PCCP·2024
Same author

Combined First-Principles and Experimental Study on the Microstructure and Mechanical Characteristics of the Multicomponent Additive-Manufactured Ti-35Nb-7Zr-5Ta Alloy.

ACS omega·2023

Related Experiment Video

Updated: Apr 20, 2026

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

13.5K

On the time scale associated with Monte Carlo simulations.

Kristof M Bal1, Erik C Neyts1

  • 1Department of Chemistry, University of Antwerp, Research Group PLASMANT, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium.

The Journal of Chemical Physics
|November 29, 2014
PubMed
Summary

The time-stamped force-bias Monte Carlo (tfMC) method significantly accelerates atomistic simulations by reducing activation barriers in solid-state systems. This method offers substantial timescale boosts without requiring system-specific adjustments.

More Related Videos

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

4.7K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.7K

Related Experiment Videos

Last Updated: Apr 20, 2026

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

13.5K
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

4.7K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.7K

Area of Science:

  • Computational physics and chemistry
  • Materials science
  • Statistical mechanics

Background:

  • Uniform-acceptance force-bias Monte Carlo (fbMC) methods enhance atomistic simulations for studying phenomena like phase transitions.
  • The time-stamped force-bias Monte Carlo (tfMC) method was recently developed with an estimated effective timescale, but its full capabilities remain unclear.

Purpose of the Study:

  • To explicitly quantify the effective timescale accessible by the tfMC method across diverse systems.
  • To gain new insights into the operational mechanisms of tfMC.
  • To evaluate tfMC's potential and limitations compared to molecular dynamics.

Main Methods:

  • Application of tfMC to a single-particle model, Lennard-Jones liquid, adatom on Cu(100), silicon crystal with defects, and a defective graphene sheet.
  • Explicit quantification of the effective timescale achieved by tfMC.
  • Comparison of tfMC performance with molecular dynamics.

Main Results:

  • tfMC achieves timescale boosts of up to three orders of magnitude for solid-state systems compared to molecular dynamics.
  • The method effectively lowers apparent activation barriers without requiring system-specific input.
  • Insights into the mechanisms underlying tfMC's success were obtained.

Conclusions:

  • tfMC is a powerful tool for accelerating atomistic simulations of solid-state systems.
  • The method's ability to reduce activation barriers is key to its performance.
  • Careful consideration of tfMC's limitations regarding explicit dynamics and reaction mechanisms is necessary.