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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

207
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...
207

You might also read

Related Articles

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

Sort by
Same author

Bringing discrete-time Langevin splitting methods into agreement with thermodynamics.

The Journal of chemical physics·2021
Same author

Building intuition for binding free energy calculations: Bound state definition, restraints, and symmetry.

The Journal of chemical physics·2021
Same author

Publisher's Note: "Building intuition for binding free energy calculations: Bound state definition, restraints, and symmetry" [J. Chem. Phys. 154, 204101 (2021)].

The Journal of chemical physics·2021
Same author

Multilevel summation for periodic electrostatics using B-splines.

The Journal of chemical physics·2021
Same author

Classical molecular dynamics.

The Journal of chemical physics·2021
Same author

A critical perspective on Markov state model treatments of protein-protein association using coarse-grained simulations.

The Journal of chemical physics·2021

Related Experiment Video

Updated: Dec 13, 2025

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

12.2K

Scalable molecular dynamics on CPU and GPU architectures with NAMD.

James C Phillips1, David J Hardy1, Julio D C Maia1

  • 1NIH Center for Macromolecular Modeling and Bioinformatics, Theoretical and Computational Biophysics Group, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.

The Journal of Chemical Physics
|August 6, 2020
PubMed
Summary

NAMD is a high-performance molecular dynamics program for simulating large biological systems on various architectures. It offers efficient tools for equilibrium and enhanced-sampling simulations, making complex biological modeling more accessible.

More Related Videos

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.9K
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

495

Related Experiment Videos

Last Updated: Dec 13, 2025

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

12.2K
Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.9K
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

495

Area of Science:

  • Computational Biology
  • Biophysics
  • Molecular Modeling

Background:

  • Molecular dynamics simulations are crucial for understanding biological processes at the atomic level.
  • Simulating large biological systems requires computationally efficient and scalable software.
  • Existing tools often face limitations in handling system size and complexity.

Purpose of the Study:

  • To review the features of NAMD, a molecular dynamics program designed for high-performance simulations.
  • To detail NAMD's capabilities for both equilibrium and enhanced-sampling simulations.
  • To discuss NAMD's roadmap for future development, focusing on GPU acceleration and large-scale simulations.

Main Methods:

  • NAMD utilizes C++ and Charm++ for optimal performance on CPU and GPU architectures.
  • It supports multiple thermodynamic ensembles and popular biomolecular force fields (CHARMM, AMBER, OPLS, GROMOS).
  • Key features include efficient handling of long-range electrostatics, thermodynamic control, external potentials, and QM/MM descriptions.

Main Results:

  • NAMD provides scalable performance on supercomputers and commodity clusters.
  • The program offers a versatile suite of algorithms for numerical efficiency in simulations.
  • Enhanced-sampling features facilitate the determination of free-energy differences for transformations.

Conclusions:

  • NAMD is a powerful, versatile, and efficient molecular dynamics program for large-scale biological simulations.
  • Ongoing development aims to enhance GPU performance, enable billion-atom simulations, and simplify usage.
  • NAMD is freely available with source code, promoting accessibility in research.