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

Machines01:19

Machines

579
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
579
Machines: Problem Solving II01:30

Machines: Problem Solving II

672
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.
672
Machines: Problem Solving I01:22

Machines: Problem Solving I

715
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
715
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Dynamic Equilibrium02:20

Dynamic Equilibrium

62.9K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
62.9K
Excitation-Contraction Coupling in Skeletal Muscles01:20

Excitation-Contraction Coupling in Skeletal Muscles

15.2K
Excitation-contraction coupling is a series of events that occur between generating an action potential and initiating a muscle contraction. It occurs at the triad, a structure found in skeletal muscle fibers that comprise a T-tubule and terminal cisternae of the sarcoplasmic reticulum on each side. These triads are visible in longitudinally sectioned muscle fibers. They are typically located at the A-I junction — the junction between the A and I bands of the sarcomere.
When an action...
15.2K

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

Real-Time Vibrational Spectroscopy Reveals an Inversion Transition State in the Photoisomerization of Phenylazoimidazole.

The journal of physical chemistry letters·2026
Same journal

Precursor-Directed Self-Assembly in Hydrothermal Carbon Nitride Nanostructures Revealed by Nano-FTIR.

The journal of physical chemistry letters·2026
Same journal

Correction to "Equation-of-Motion Block-Correlated Coupled Cluster Method for Excited Electronic States of Strongly Correlated Systems".

The journal of physical chemistry letters·2026
Same journal

Rationalizing Stacking-Dependent Charge Injection Dynamics in Radical-Based Organic Light-Emitting Diodes.

The journal of physical chemistry letters·2026
Same journal

Bottom-Up Formation of the Simplest Geminal Thiol─Methanedithiol (CH<sub>2</sub>(SH)<sub>2</sub>)─and the Methyl Hydrodisulfide (H<sub>3</sub>CSSH) Isomer in Interstellar Analogue Ices.

The journal of physical chemistry letters·2026
Same journal

Trion Mediated Sequential Charge Separation in Functionalized CsPbBr<sub>3</sub>/AgInS<sub>2</sub> Hybrid Nanocrystals.

The journal of physical chemistry letters·2026
See all related articles

Related Experiment Video

Updated: Feb 5, 2026

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

2.6K

Nonadiabatic Excited-State Dynamics with Machine Learning.

Pavlo O Dral1, Mario Barbatti2, Walter Thiel1

  • 1Max-Planck-Institut für Kohlenforschung , Kaiser-Wilhelm-Platz 1 , 45470 Mülheim an der Ruhr , Germany.

The Journal of Physical Chemistry Letters
|September 12, 2018
PubMed
Summary
This summary is machine-generated.

Machine learning accurately simulates excited-state dynamics. This method speeds up complex quantum system simulations by using approximate machine learning potentials, reducing computational time significantly.

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K

Related Experiment Videos

Last Updated: Feb 5, 2026

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

2.6K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K

Area of Science:

  • Quantum Chemistry
  • Computational Physics
  • Machine Learning Applications

Background:

  • Simulating nonadiabatic excited-state dynamics is computationally intensive.
  • Accurate modeling requires capturing quantum effects like decoherence.
  • Current methods face challenges with high-dimensional systems.

Purpose of the Study:

  • To demonstrate machine learning's capability in reproducing complex quantum dynamics.
  • To develop a computationally efficient approach for simulating excited-state dynamics.
  • To reduce the simulation time for realistic, high-dimensional quantum systems.

Main Methods:

  • Utilized machine learning (ML) to create approximate potentials for adiabatic states.
  • Employed decoherence-corrected fewest switches surface hopping (FSSH) for dynamics.
  • Validated the ML approach against reference simulations using spin-boson models.

Main Results:

  • Achieved accurate reproduction of nonadiabatic excited-state dynamics in a 1-D model.
  • Demonstrated significant reduction in simulation time for high-dimensional systems.
  • ML potentials effectively captured dynamics comparable to reference simulations.

Conclusions:

  • Machine learning offers a powerful tool for accelerating quantum dynamics simulations.
  • The proposed ML approach is feasible for complex, multi-dimensional systems.
  • This method holds promise for advancing computational studies in quantum chemistry and physics.