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

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.1K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.1K
The Uncertainty Principle04:08

The Uncertainty Principle

29.3K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
29.3K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.3K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.3K
MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

12.8K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
12.8K
Potential Due to a Polarized Object01:29

Potential Due to a Polarized Object

536
A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
536
Molecular Orbital Theory I02:35

Molecular Orbital Theory I

39.1K
Overview of Molecular Orbital Theory
39.1K

You might also read

Related Articles

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

Sort by
Same author

Estimating particle size and velocity from fluorescence pulses: A practical validation study of flow cytometry signals analysis.

PloS one·2026
Same author

Estimating single-cell elastic modulus in a serial microfluidic cytometer from time-of-flight and fluorescence signals analysis.

Lab on a chip·2026
Same author

Spatial Encoding with Amplitude Modulation in Serial Flow Cytometry.

Sensors (Basel, Switzerland)·2026
Same author

Per-Event Uncertainty Quantification for Flow Cytometry Using Calibration Beads.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2025
Same author

Uncertainty Quantification of Fluorescence Signals in Flow Cytometry Part I: An Analytical Perspective Beyond Q and B.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2025
Same author

Uncertainty Quantification of Fluorescence Signals for Cytometry Part II: Comparison of Serial and Traditional Flow Cytometers.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2025

Related Experiment Video

Updated: Nov 1, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.4K

Towards a priori uncertainty quantification in coarse-grained molecular dynamics: Generalized multipole potentials.

Paul N Patrone1, Andrew M Dienstfrey1, Geoffrey B McFadden1

  • 1Applied and Computational Mathematics Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg MD, 20899, USA.

AIAA Journal. American Institute of Aeronautics and Astronautics
|June 21, 2021
PubMed
Summary

This study introduces an analytical method for coarse-graining rigid-body systems, enabling accuracy assessment of reduced-order models without costly simulations. This approach aids in justifying computational materials science model development costs.

More Related Videos

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.4K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.0K

Related Experiment Videos

Last Updated: Nov 1, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.4K
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.4K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.0K

Area of Science:

  • Computational Materials Science
  • Molecular Dynamics Simulations

Background:

  • Coarse-graining (CG) methods in computational materials science often lack upfront uncertainty quantification (UQ) tools.
  • This absence hinders the adoption of CG techniques due to unknown model accuracy and calibration costs.

Purpose of the Study:

  • To develop an analytical method for coarse-graining rigid-body systems.
  • To provide a priori UQ for reduced-order models in CG molecular dynamics (MD).
  • To establish a mathematical foundation for assessing CG force field quality without simulations.

Main Methods:

  • Developed an analytical coarse-graining approach for rigid-body systems.
  • Derived corresponding intermolecular potentials with controllable accuracy relative to atomistic models.
  • Validated the method using simulated trajectories.

Main Results:

  • The analytical method provides a mathematical basis for evaluating CG force field quality a priori.
  • It allows understanding atomistic systems as limits of reduced-order models.
  • Simulations confirmed the approach's validity at the trajectory level.

Conclusions:

  • The presented method offers a way to assess CG model accuracy before extensive simulations.
  • It addresses the need for UQ in coarse-grained molecular dynamics.
  • Further work is needed for coarse-graining fully non-rigid systems.