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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

36.3K
VSEPR Theory for Determination of Electron Pair Geometries
36.3K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

262
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
262
Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

24.4K
An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
24.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

152
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
152
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

776
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
776

You might also read

Related Articles

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

Sort by
Same author

Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation.

Protein science : a publication of the Protein Society·2026
Same author

Correlated clustering and projection for dimensionality reduction.

Machine learning: science and technology·2026
Same author

Polyphosphates-Based Cathode-Electrolyte Interphase for 4.65 V LiCoO<sub>2</sub>.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Manifold topological deep learning for biomedical data.

Nature communications·2026
Same author

CAP: Commutative algebra prediction of protein-nucleic acid binding affinities.

Machine learning: science and technology·2026
Same author

Microphase-Separated Elastomers Enable Synergistic Dispersion and Coalescence Control in Conductive Pastes for Fine Printing.

Advanced materials (Deerfield Beach, Fla.)·2026

Related Experiment Video

Updated: Sep 19, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.2K

Enhancing energy predictions in multi-atom systems with multiscale topological learning.

Dong Chen1,2, Rui Wang3, Guo-Wei Wei2,4,5

  • 1School of Advanced Materials, Peking University, Shenzhen Graduate School Shenzhen 518055 China panfeng@pkusz.edu.cn.

Journal of Materials Chemistry. A
|June 16, 2025
PubMed
Summary

This study introduces a topological learning framework to predict lithium atom interactions in battery materials. The method accurately captures complex many-body interactions, enhancing energy predictions for improved battery performance.

More Related Videos

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

10.6K
Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
08:04

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

8.6K

Related Experiment Videos

Last Updated: Sep 19, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.2K
Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

10.6K
Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
08:04

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

8.6K

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Battery Technology

Background:

  • Lithium is vital for high-energy-density batteries, but understanding lithium atom interactions in clusters is complex.
  • Predictive accuracy for multi-atom systems is limited by scarce material science data.
  • Existing methods struggle with the exponentially increasing complexity of lithium atom interactions.

Purpose of the Study:

  • To develop an interpretable topological learning framework for accurate energy predictions in multi-atom lithium systems.
  • To enhance the understanding of lithium atom cluster interactions.
  • To overcome data limitations in material science for predictive modeling.

Main Methods:

  • Application of Persistent Topological Laplacians (PTLs), a multiscale topological method.
  • Analysis of a dataset comprising 136,287 lithium clusters.
  • Utilizing a topological learning framework to capture many-body interactions.

Main Results:

  • The PTL method effectively captures intrinsic properties of many-body interactions.
  • Persistent topological features and geometric nuances in complex material systems were uncovered.
  • The framework demonstrated alignment with traditional many-body theories.

Conclusions:

  • The proposed PTL framework enhances prediction accuracy for multi-atom systems.
  • This approach offers a robust method for analyzing complex many-body interactions in materials.
  • The findings contribute to optimizing battery performance, safety, and longevity through improved lithium cluster understanding.