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

Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Non-conservative Forces01:17

Non-conservative Forces

Non-conservative forces are dissipative forces such as friction or air resistance. These forces take energy away from a system as it progresses. Unlike conservative forces, non-conservative forces do not have potential energy associated with them. This is because the energy is lost to the system and cannot be turned into useful work later.
Also unlike their conservative counterparts, they are path-dependent; where the object starts and stops does matter. For example, a grinding wheel applies a...
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
Reaction Mechanisms: The Steady-State Approximation01:26

Reaction Mechanisms: The Steady-State Approximation

The steady-state approximation, also referred to as the quasi-steady-state approximation to differentiate it from a true steady state, is a widely used method for simplifying calculations in complex reaction mechanisms. This approach is particularly useful when dealing with multi-step reactions that involve reverse reactions or several steps, which can significantly increase mathematical complexity and make the reactions nearly unsolvable analytically.The steady-state approximation operates on...
Action Potential01:14

Action Potential

Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

You might also read

Related Articles

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

Sort by
Same author

Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations.

Communications chemistry·2026
Same author

Computational Study of Heme <i>b</i><sub>595</sub> to Heme <i>d</i> Electron Transfer in <i>E. coli</i> Cytochrome <i>bd</i>-I Oxidase.

Journal of chemical information and modeling·2026
Same author

Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models Using Multiple Time Steps and Distillation.

The journal of physical chemistry letters·2026
Same author

Quantum speedup for nonreversible Markov chains.

Nature communications·2025
Same author

Probing the partition function for temperature-dependent potentials with nested sampling.

The Journal of chemical physics·2025
Same author

Targeting RNA with small molecules using state-of-the-art methods provides highly predictive affinities of riboswitch inhibitors.

Communications biology·2025

Related Experiment Video

Updated: Jun 7, 2026

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

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Nonconservative

Nicolaï Gouraud1, Côme Cattin2, Thomas Plé2

  • 1Qubit Pharmaceuticals, Advanced Research Department, 75014 Paris, France.

Journal of Chemical Theory and Computation
|June 6, 2026
PubMed
Summary

The new Distilled Multi-Time-Step (DMTS) approach using nonconservative (NC) forces accelerates atomistic molecular dynamics (MD) simulations. This method enhances stability and efficiency, achieving significant speedups for neural network potentials.

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

Related Experiment Videos

Last Updated: Jun 7, 2026

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

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

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Artificial Intelligence in Science

Background:

  • Atomistic molecular dynamics (MD) simulations are crucial for understanding molecular behavior.
  • Accelerating MD simulations is essential for tackling larger and more complex systems.
  • Foundation neural network models offer promise for accurate molecular simulations.

Purpose of the Study:

  • To introduce the Distilled Multi-Time-Step (DMTS) approach with nonconservative (NC) forces for accelerated MD simulations.
  • To enhance the efficiency and stability of simulations using neural network potentials.
  • To demonstrate the applicability and performance of the DMTS-NC scheme.

Main Methods:

  • Development of the DMTS-NC strategy, employing a dual-level Reversible Reference System Propagator Algorithm (RESPA) formalism.
  • Coupling an accurate conservative potential with a simplified distilled representation for nonconservative forces.
  • Incorporation of physical priors like equivariance and force cancellation into the distilled architecture.
  • Combination with hydrogen mass repartitioning (HMR) and High Hydrogen Friction (HHF) for extended time steps.

Main Results:

  • The DMTS-NC scheme achieves 15-30% greater speedup compared to its conservative counterpart.
  • The approach demonstrates excellent agreement with force data, limiting discrepancies between models.
  • Extended time steps up to 10 fs are maintained with stability and accuracy.
  • Speedups ranging from 3.66 to 5.64 were observed when distilling MACE-OFF23.

Conclusions:

  • The DMTS-NC approach offers a robust, stable, and efficient method for accelerating atomistic MD simulations.
  • It requires no fine-tuning, simplifying implementation and maximizing efficiency.
  • The method is versatile, applicable to any neural network potential (NNPs) and computationally intensive approaches.