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

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

469
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
469
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

406
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
406
Non-uniform Circular Motion01:22

Non-uniform Circular Motion

7.2K
In uniform circular motion, the particle executing circular motion has a constant speed, and the circle is at a fixed radius. However, not all circular motion occurs at a constant speed. A particle can travel in a circle and speed up or slow down, showing an acceleration in the direction of motion. In that case, the motion is called non-uniform circular motion, and an additional acceleration is introduced, which is in the direction tangential to the circle. 
For example, such...
7.2K
Curvilinear Motion: Polar Coordinates01:27

Curvilinear Motion: Polar Coordinates

370
In polar coordinates, the motion of a particle follows a curvilinear path. The radial coordinate symbolized as 'r,' extends outward from a fixed origin to the particle, while the angular coordinate, 'θ,' measured in radians, represents the counterclockwise angle between a fixed reference line and the radial line connecting the origin to the particle.
The particle's location is described using a unit vector along the radial direction. Deriving the particle's position...
370
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

462
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
462
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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...
56

You might also read

Related Articles

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

Sort by
Same author

Feynman Path Tube-Guided Surrogate Ring-Polymer Dynamics for Quantum Nuclear Transfer in the Gas Phase and on Surfaces.

Journal of chemical theory and computation·2026
Same author

Catalyzing the Polysulfide Redox Chemistry in Lithium-Sulfur Batteries: Mechanisms Bridging First-Principles Theory and Experimental Characterization.

ACS applied materials & interfaces·2026
Same author

Multiconfiguration Pair-Density Functional Theory with Quantum Embedding Predicts Correct CO Adsorption Sites on Copper Facets.

The journal of physical chemistry letters·2025
Same author

Fluorinated Imidazolidinium Cations as a Fluorine-Lean Interface Repairing Agent for Li-Metal Batteries.

ACS applied materials & interfaces·2025
Same author

Direct Formation of C<sub>3</sub> Oxygenates through Photocatalytic CH<sub>4</sub>-CO Coupling.

Journal of the American Chemical Society·2025
Same author

Physical Prior Mean-Driven Bayesian Committee Molecular Dynamics (BCMD): From Born-Oppenheimer Dynamics to Curvature-Guided Non-Adiabatic Dynamics.

Journal of chemical theory and computation·2025

Related Experiment Video

Updated: Jul 9, 2025

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
09:46

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

12.7K

Exploring torsional conformer space with physical prior mean function-driven meta-Gaussian processes.

Chong Teng1, Daniel Huang2, Elizabeth Donahue1

  • 1Department of Chemistry, Boston College, Chestnut Hill, Massachusetts 02467, USA.

The Journal of Chemical Physics
|December 5, 2023
PubMed
Summary

We developed physics-driven meta-Gaussian processes (meta-GPs) for efficient molecular conformational search. This novel method enhances conformer discovery and potential energy surface learning for small molecules.

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

998

Related Experiment Videos

Last Updated: Jul 9, 2025

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
09:46

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

12.7K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

998

Area of Science:

  • Computational Chemistry
  • Molecular Modeling
  • Machine Learning in Chemistry

Background:

  • Accurate thermodynamic functions require exploring the conformational space of small molecules, identifying all local minima.
  • Traditional methods optimize structures individually, lacking transferability for efficient multi-conformer optimization.
  • Exploring potential energy surfaces (PES) for unique conformers is crucial but computationally intensive.

Purpose of the Study:

  • To introduce a novel physics-driven meta-Gaussian processes (meta-GPs) method for systematic conformational space exploration.
  • To enable efficient and comprehensive discovery of molecular conformers and accurate PES generation.
  • To demonstrate the transfer learning capabilities of meta-GPs with adaptive priors in torsional space.

Main Methods:

  • Development of meta-Gaussian processes (meta-GPs) incorporating physical surrogates for universal application across conformer optimization.
  • Dynamic selection of prior mean functions based on optimization history and progress for Bayesian learning.
  • Systematic benchmarking of meta-GP variants against conventional optimizers for brute-force conformational search of amino acids.

Main Results:

  • Meta-GPs demonstrated superior efficiency, comprehensiveness, and conformer distribution compared to non-surrogate and non-meta-GP methods.
  • The method successfully generated high-quality PESs in torsional space with minimal training data.
  • Meta-GPs showed significant advantages in exploring the conformational space of small molecules, including amino acids.

Conclusions:

  • Meta-Gaussian processes offer a powerful and efficient approach for systematic conformational analysis of small molecules.
  • The physics-driven nature and adaptive priors of meta-GPs facilitate effective transfer learning in molecular modeling.
  • This work presents a promising avenue for advancing computational chemistry through physics-based machine learning.