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

117
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...
117
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

248
Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
248
Improving Translational Accuracy02:07

Improving Translational Accuracy

12.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
12.0K
Machines: Problem Solving II01:30

Machines: Problem Solving II

426
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
426
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

36.9K
VSEPR Theory for Determination of Electron Pair Geometries
36.9K
Molecular Kinetic Energy01:21

Molecular Kinetic Energy

5.2K
The word "gas" comes from the Flemish word meaning "chaos," first used to describe vapors by the chemist J. B. van Helmont. Consider a container filled with gas, with a continuous and random motion of molecules. During collisions, the velocity component parallel to the wall is unchanged, and the component perpendicular to the wall reverses direction but does not change in magnitude. If the molecule’s velocity changes in the x-direction, then its momentum is changed.
5.2K

You might also read

Related Articles

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

Sort by
Same author

Aitomia: An Agentic Framework for AI-Driven Atomistic and Quantum Chemical Simulations.

Journal of chemical theory and computation·2026
Same author

The Newton-X platform for mixed quantum-classical dynamics.

Physical chemistry chemical physics : PCCP·2026
Same author

The Hidden Routes of DNA Photostability: Charge and Proton Transfer in Excited Cytosine-Guanine Tetramers.

The journal of physical chemistry letters·2026
Same author

Integrating Machine Learning Interatomic Potentials with MMPBSA for Accurate Protein-Ligand Binding Free Energy Calculations.

The journal of physical chemistry. B·2026
Same author

OMNI-P2x universal neural network potential for excited-state simulations.

Nature communications·2026
Same author

Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics.

Journal of chemical theory and computation·2026
Same journal

Enhanced and selective oxygen reduction by iron porphyrin with a biguanide residue in the second coordination sphere.

Chemical science·2026
Same journal

Excited-state orbital angular momentum enables all-optical molecular spin coherence.

Chemical science·2026
Same journal

Polyvinyl-based hole-transporting materials processed with non-destructive and green solvents for tin-lead perovskite solar cells and all-perovskite tandems.

Chemical science·2026
Same journal

Pd-catalyzed regio- and enantioselective allylation of cyclic allylboronates.

Chemical science·2026
Same journal

Covalent polyoxometalate-polyimide hybridization: multi-scale molecular engineering toward high-performance sodium-ion battery anodes.

Chemical science·2026
Same journal

Catalytic visible light-driven alkane dehydrogenation by a di-uranyl germanotungstate.

Chemical science·2026
See all related articles

Related Experiment Video

Updated: Oct 10, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.1K

Choosing the right molecular machine learning potential.

Max Pinheiro1, Fuchun Ge2, Nicolas Ferré1

  • 1Aix Marseille University, CNRS, ICR Marseille France max.pinheiro-jr@univ-amu.fr mario.barbatti@univ-amu.fr.

Chemical Science
|December 9, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning potentials accelerate molecular simulations. This study evaluates popular machine learning potentials for accuracy and computational cost, guiding users in selecting the best option.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Related Experiment Videos

Last Updated: Oct 10, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Area of Science:

  • Computational chemistry
  • Materials science
  • Quantum mechanics

Background:

  • Quantum-chemistry simulations offer atomistic insights into molecular processes, yielding reaction rates and spectra.
  • Machine learning potentials (MLPs) aim to reduce computational costs for these simulations.
  • A wide array of MLPs necessitates guidance for selection and development.

Purpose of the Study:

  • To evaluate the performance of popular machine learning potentials.
  • To compare MLPs based on accuracy and computational cost.
  • To provide structured information for non-specialists to understand and choose MLPs.

Main Methods:

  • Performance evaluation of selected machine learning potentials.
  • Comparative analysis of accuracy metrics.
  • Assessment of computational efficiency.

Main Results:

  • Quantitative comparison of accuracy across different machine learning potentials.
  • Benchmarking of computational cost for various MLPs.
  • Identification of strengths and weaknesses of popular MLPs.

Conclusions:

  • Guidance provided for selecting appropriate machine learning potentials.
  • Insights into the trade-offs between accuracy and cost for different MLPs.
  • Informed decision-making for researchers regarding MLP utilization and development.