Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Electrical Conductivity

Electrical Conductivity

In perfect conductors, the electric field inside is always zero due to the abundance of free electrons, which nullify any field by flowing. As a result, any residual charge resides on the surface.
In a practical conductor, an applied electric field may be sustained, causing a flow of electrons, which produce a current. The differential form of the current, the current density, is related to the electric field.
More generally, it is related to the force per unit charge, which involves the...

Trends in Lattice Energy: Ion Size and Charge

Trends in Lattice Energy: Ion Size and Charge

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:

You might also read

Related Articles

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

Sort by

Same author

GCSAM: Gradient Centralized Sharpness Aware Minimization.

IEEE access : practical innovations, open solutions·2025

Same author

Automated Skin Cancer Report Generation via a Knowledge-Distilled Vision-Language Model.

IEEE access : practical innovations, open solutions·2025

Same author

Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification.

IEEE access : practical innovations, open solutions·2025

Same author

BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations.

IEEE access : practical innovations, open solutions·2023

Same author

SepNet: A neural network for directionally correlated data.

Neural networks : the official journal of the International Neural Network Society·2022

Same author

A Probabilistic Characterization of Negative Definite Functions.

High dimensional probability·2020

Same journal

Toward Cybersecurity Testing and Monitoring of IoT Ecosystems.

SN computer science·2026

Same journal

Voxel-based Deep Regression for Enhanced Body Composition Estimation from 3D Body Scans.

SN computer science·2026

Same journal

Detecting Adverse Drug Events in Social Media: A Brief Literature Review.

SN computer science·2026

Same journal

TRAM: The Telecommunications-Related AcciMap Method.

SN computer science·2026

Same journal

A Combinatorial Approach to Synthetic Data Generation for Machine Learning.

SN computer science·2026

Same journal

To Signal or Not to Signal? A Non-cooperative Game-Theoretic Approach to Discretionary Communication Between Road Users.

SN computer science·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 18, 2025

Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy

Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy

Published on: January 20, 2023

Contrastive Metric Learning for Lithium Super-ionic Conductor Screening.

Boyu Zhang^1,2, Shuo Wang³, Fuchang Gao⁴

¹Institute for Modeling Collaboration and Innovation, University of Idaho, 875 Perimeter Dr MS 1122, Moscow, ID 83844-1122, USA.

SN Computer Science

|August 23, 2023

Summary

This summary is machine-generated.

This study introduces a new machine learning framework to identify high-conductivity materials for lithium-ion batteries. The method effectively screens materials even with limited data, improving battery development.

Keywords:

Conductor screening Contrastive learning Graph neural network Material science Metric learning

More Related Videos

1,3,5-Triphenylbenzene and Corannulene as Electron Receptors for Lithium Solvated Electron Solutions

1,3,5-Triphenylbenzene and Corannulene as Electron Receptors for Lithium Solvated Electron Solutions

Published on: October 10, 2016

Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature

Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature

Published on: December 20, 2016

Related Experiment Videos

Last Updated: Jul 18, 2025

Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy

Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy

Published on: January 20, 2023

1,3,5-Triphenylbenzene and Corannulene as Electron Receptors for Lithium Solvated Electron Solutions

1,3,5-Triphenylbenzene and Corannulene as Electron Receptors for Lithium Solvated Electron Solutions

Published on: October 10, 2016

Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature

Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature

Published on: December 20, 2016

Area of Science:

Materials Science
Electrochemistry
Computer Science

Background:

High-performance lithium-ion batteries are crucial for modern technology.
Materials with high electrical conductivity are essential for advanced battery development.
Predicting high conductors is challenging due to a lack of validated samples.

Purpose of the Study:

To develop an effective metric-learning framework for screening high-conductivity materials.
To address the challenge of limited validated conductor samples in machine learning models.
To improve the prediction accuracy of conductive materials for battery applications.

Main Methods:

Utilized a Siamese network to map material structures into an optimized feature space.
Employed an instance-based method for classifying input material samples.
Developed a metric-learning framework for direct high conductor screening.

Main Results:

The proposed method effectively extracts knowledge from imbalanced datasets.
Demonstrated good performance and generalization ability in conductor screening.
Successfully mapped material structures to a discriminative feature space.

Conclusions:

The metric-learning framework offers a viable solution for high conductor screening.
The approach shows promise for accelerating the discovery of advanced battery materials.
Effective handling of imbalanced data is a key strength of the proposed method.