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

237
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:
237
Mason's Rule01:20

Mason's Rule

518
Mason's rule is a powerful tool in control systems and signal processing. It simplifies the calculation of transfer functions from signal-flow graphs. This method leverages various elements, including loop gains, forward-path gains, and non-touching loops, to determine the transfer function efficiently.
Loop gain is determined by identifying and tracing a path from a node back to itself. This involves computing the product of branch gains along the loop. Each loop's gain is crucial for...
518
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
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...
107

You might also read

Related Articles

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

Sort by
Same author

Automating Computational Chemistry Workflows via OpenClaw and Domain-Specific Skills.

Journal of chemical theory and computation·2026
Same author

Automated Force Field Developer and Optimizer Platform: Torsion Reparameterization.

Journal of chemical information and modeling·2026
Same author

A Relative Binding Free Energy Framework for Structurally Dissimilar Molecules.

Journal of chemical information and modeling·2026
Same author

SAMTI: Sampling Adaptive Thermodynamic Integration for Alchemical Free Energy Calculations.

The journal of physical chemistry. B·2025
Same author

DPDispatcher: Scalable HPC Task Scheduling for AI-Driven Science.

Journal of chemical information and modeling·2025
Same author

dpdata: A Scalable Python Toolkit for Atomistic Machine Learning Data Sets.

Journal of chemical information and modeling·2025
Same journal

Selective Effects of Backbone Cyclization and Disulfide Bonding as Global Covalent Constraints on the Conformational Ensemble of Sunflower Trypsin Inhibitor-1.

The journal of physical chemistry. B·2026
Same journal

Europium Coordination Structure in Peptide Complexes Resolved with Simulation and X-ray Absorption Spectroscopy.

The journal of physical chemistry. B·2026
Same journal

Competitive Coordination and Structural Evolution of Phenylalanine-Mg<sup>2+</sup> Complexes in Microaqueous Environments: Insights from DFT and Molecular Dynamics Simulations.

The journal of physical chemistry. B·2026
Same journal

Dressing up a Magnetic Nanoparticle at Atomic Resolution: Molecular Simulation of Full Carrier Grafting by Self-Assembled Monolayers.

The journal of physical chemistry. B·2026
Same journal

Ferroelectricity in Dipolar Liquids: The Role of Annealed Positional Disorder.

The journal of physical chemistry. B·2026
Same journal

Computational Insights into the Antiviral Properties of the Antimicrobial Peptide β-Amyloid.

The journal of physical chemistry. B·2026
See all related articles

Related Experiment Video

Updated: Sep 20, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

672

Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications.

Timothy J Giese1, Jinzhe Zeng2,3, Darrin M York1

  • 1Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States.

The Journal of Physical Chemistry. B
|May 26, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new machine learning potential using graph neural networks for more accurate molecular simulations. This approach improves the prediction of reaction pathways and intermediates, showing enhanced transferability compared to previous methods.

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

693
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.9K

Related Experiment Videos

Last Updated: Sep 20, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

672
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

693
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.9K

Area of Science:

  • Computational chemistry
  • Machine learning in chemistry
  • Quantum mechanics/molecular mechanics (QM/MM) simulations

Background:

  • The accuracy of QM/MM simulations is crucial for understanding complex chemical reactions.
  • Previous range-corrected Δ-machine learning potentials (ΔMLP) improved QM/MM accuracy by correcting energies and forces.
  • Deep neural networks have shown promise in enhancing simulation accuracy.

Purpose of the Study:

  • To extend the ΔMLP approach by incorporating graph neural networks, specifically the MACE architecture.
  • To evaluate the transferability and accuracy of AM1/d + MACE models for phosphoryl transesterification reactions.
  • To compare the performance of MACE against the DeepPot-SE (DP) architecture in QM/MM simulations.

Main Methods:

  • Training AM1/d + MACE models to reproduce PBE0/6-31G* QM/MM energies and forces.
  • Testing model transferability using reactions not included in the training set.
  • Calculating free energy surfaces to assess reaction pathway accuracy.
  • Varying MACE hyperparameters to study their impact on accuracy and performance.

Main Results:

  • AM1/d + MACE models accurately reproduced target free energy surfaces, outperforming AM1/d + DP models in some cases.
  • End-state AM1/d + MACE models correctly predicted a stable pentacoordinated phosphorus intermediate, even without similar structures in training data.
  • MACE architecture demonstrated improved transferability for ΔMLP models.
  • AM1/d + MACE simulations were found to be 28% slower than AM1/d QM/MM when using GPU acceleration.

Conclusions:

  • The MACE architecture offers improved transferability for ΔMLP models in QM/MM simulations.
  • Graph neural networks represent a promising direction for developing more accurate and transferable machine learning potentials.
  • The developed AM1/d + MACE models provide a more reliable approach for studying complex chemical reactions like phosphoryl transesterification.