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

Gauss's Law in Dielectrics01:17

Gauss's Law in Dielectrics

5.0K
Consider a polar dielectric placed in an external field. In such a dielectric, opposite charges on adjacent dipoles neutralize each other, such that the net charge within the dielectric is zero. When a polar dielectric is inserted in between the capacitor plates, an electric field is generated due to the presence of net charges near the edge of the dielectric and the metal plates interface. Since the external electrical field merely aligns the dipoles, the dielectric as a whole is neutral. An...
5.0K
Electrostatic Boundary Conditions in Dielectrics01:27

Electrostatic Boundary Conditions in Dielectrics

1.8K
When an electric field passes from one homogeneous medium to another, crossing the boundary between the two mediums imparts a discontinuity in the electric field. This results in electrostatic boundary conditions that depend on the type of mediums the field propagates through.
Consider a case where both the mediums across a boundary are two different dielectric materials. Recall that the electric field and electric displacement are proportional and related through the material's permittivity....
1.8K
π Electron Effects on Chemical Shift: Overview01:27

π Electron Effects on Chemical Shift: Overview

1.6K
An applied magnetic field causes loosely bound π-electrons in organic molecules to circulate, producing a local or induced diamagnetic field over a large spatial volume. As the molecules tumble in solution, the field generated by π-electrons in spherical substituents results in a zero net field. However, the net field generated by π-electrons in non-spherical substituents is not zero. The effect of this induced field depends on the orientation of the molecule with respect to B0,...
1.6K
Electronic Structure of Atoms02:28

Electronic Structure of Atoms

27.7K

An atom comprises protons and neutrons, which are contained inside the dense, central core called the nucleus, with electrons present around the nucleus. Taking into account the wave–particle duality of electrons and the uncertainty in position around the nucleus, quantum mechanics provides a more accurate model for the atomic structure. It describes atomic orbitals as the regions around the nucleus where electrons of discrete energy exist, characterized by four quantum...
27.7K
Maxwell's Equation Of Electromagnetism01:29

Maxwell's Equation Of Electromagnetism

3.9K
James Clerk Maxwell (1831–1879) was one of the major contributors to physics in the nineteenth century. Although he died young, he made major contributions to the development of the kinetic theory of gases, to the understanding of color vision, and to understanding the nature of Saturn's rings. He is probably best known for having combined existing knowledge on the laws of electricity and magnetism with his insights into a complete overarching electromagnetic theory, which is...
3.9K
Fermi Level Dynamics01:12

Fermi Level Dynamics

622
The vacuum level denotes the energy threshold required for an electron to escape from a material surface. It is usually positioned above the conduction band of a semiconductor and acts as a benchmark for comparing electron energies within various materials.
Electron affinity in semiconductors refers to the energy gap between the minimum of its conduction band and the vacuum level and it is a critical parameter in determining how easily a semiconductor can accept additional electrons.
The work...
622

You might also read

Related Articles

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

Sort by
Same author

How Water Transfers Proton to Pyrrolo[1,2-<i>a</i>]quinoxalines in the Presence of TNT: A Mechanistic Study and a Possible Method for TNT Detection.

The journal of physical chemistry. B·2026
Same author

Stacking-Induced Ferroelectricity in Tetralayer Graphene.

Nano letters·2026
Same author

Deciphering Cancer Evolution Through Genomic Profiling of Patient-Derived Xenograft Together with Matched Primary Gallbladder Cancer.

Digestive diseases and sciences·2025
Same author

Keto-enol at play: regioisomerism regulated switching in indole-naphthyl Schiff bases.

Physical chemistry chemical physics : PCCP·2025
Same author

Visualization of topological shear polaritons in gypsum thin films.

Science advances·2025
Same author

Rare-Earth Ion Intercalation in Graphene via Thermal and Electrostatic Control.

Advanced materials (Deerfield Beach, Fla.)·2025
Same journal

Journal research data policies in materials science.

Digital discovery·2026
Same journal

Text-to-flowsheet: an LLM-assisted pipeline for expert-level digitization and automated simulation of chemical processes.

Digital discovery·2026
Same journal

<i>optimade-maker</i>: automated generation of interoperable materials APIs from static datasets.

Digital discovery·2026
Same journal

RobInHood: a robotic chemist in a fume hood.

Digital discovery·2026
Same journal

Molecular arms race classifier for decrypting venom peptide and ion channel interactions.

Digital discovery·2026
Same journal

Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models.

Digital discovery·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

A Standard and Reliable Method to Fabricate Two-Dimensional Nanoelectronics
07:12

A Standard and Reliable Method to Fabricate Two-Dimensional Nanoelectronics

Published on: August 28, 2018

10.3K

Deep learning methods for 2D material electronic properties.

Artem Mishchenko1, Anupam Bhattacharya1, Xiangwen Wang1

  • 1Department of Physics and Astronomy, University of Manchester Manchester UK artem.mishchenko@manchester.ac.uk anupam.bhattacharya@manchester.ac.uk.

Digital Discovery
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning (DL) significantly enhances the prediction of electronic structures in 2D materials, overcoming unique computational challenges. This accelerates the discovery of novel quantum phenomena and material properties.

More Related Videos

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.1K
Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
13:56

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

8.0K

Related Experiment Videos

Last Updated: Jan 8, 2026

A Standard and Reliable Method to Fabricate Two-Dimensional Nanoelectronics
07:12

A Standard and Reliable Method to Fabricate Two-Dimensional Nanoelectronics

Published on: August 28, 2018

10.3K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.1K
Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
13:56

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

8.0K

Area of Science:

  • Materials Science
  • Computational Physics
  • Artificial Intelligence

Background:

  • Two-dimensional (2D) materials possess unique electronic properties and computational challenges.
  • Understanding and predicting these electronic structures is crucial for materials discovery.

Purpose of the Study:

  • To review the impact of deep learning (DL) on understanding and predicting electronic structures in 2D materials.
  • To highlight DL approaches and their success in accelerating materials science research.

Main Methods:

  • Physics-aware deep learning models
  • Generative AI for materials design
  • Inverse design strategies
  • Analysis of quantum transport phenomena

Main Results:

  • DL significantly improves predictions of band structures and density of states.
  • DL accelerates the discovery of emergent quantum phenomena, topology, and superconductivity.
  • Autonomous materials exploration is facilitated by DL methods.

Conclusions:

  • Deep learning offers powerful tools for advancing 2D materials research.
  • Future work requires data standardization and integrated theoretical, DL, and experimental frameworks.