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Related Concept Videos

Band Theory02:35

Band Theory

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When two or more atoms come together to form a molecule, their atomic orbitals combine and molecular orbitals of distinct energies result. In a solid, there are a large number of atoms, and therefore a large number of atomic orbitals that may be combined into molecular orbitals. These groups of molecular orbitals are so closely placed together to form continuous regions of energies, known as the bands.
The energy difference between these bands is known as the band gap.
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Isolated atoms have discrete energy levels that are well described by the Bohr model. And, it quantifies the energy of an electron in a hydrogen atom as En. Higher quantum numbers 'n' yield less negative, closer electron energy levels.
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Bandpass Sampling01:17

Bandpass Sampling

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
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Semiconductors01:22

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There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
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VSEPR Theory for Determination of Electron Pair Geometries
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The Fermi-Dirac function is represented by an S-shaped curve indicating the probability of an energy state being occupied by an electron at a given temperature. The Fermi level is the energy level at which there is a fifty percent chance of finding an electron, and it is positioned between the lower-energy valence band and the higher-energy conduction band.
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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
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Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction.

Gyoung S Na1, Seunghun Jang1, Yea-Lee Lee1

  • 1Chemical Data-Driven Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114, Korea.

The Journal of Physical Chemistry. A
|December 7, 2020
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Summary
This summary is machine-generated.

Machine learning accurately predicts crystalline material band gaps using novel tuplewise graph neural networks (TGNN). This approach offers a cost-effective, high-accuracy alternative to traditional methods for materials science discovery.

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Area of Science:

  • Computational Materials Science
  • Machine Learning in Chemistry
  • Solid-State Physics

Background:

  • Open-access material databases enable new computational approaches.
  • Accurate prediction of band gaps is crucial for materials discovery.
  • Traditional methods can be computationally intensive.

Purpose of the Study:

  • To develop a machine learning model for accurate band gap prediction in crystalline compounds.
  • To introduce a novel tuplewise graph neural network (TGNN) for crystal structure representation.
  • To leverage crystal-level properties as input features for enhanced prediction.

Main Methods:

  • Utilized open-access material databases.
  • Developed and applied tuplewise graph neural networks (TGNN).
  • TGNN automatically generates input representations and incorporates crystal-level properties.

Main Results:

  • Achieved highly accurate band gap predictions at hybrid functional and GW approximation levels.
  • Demonstrated high accuracy across multiple material datasets with reduced computational cost.
  • Provided a dataset of 45,835 predicted GW band gaps.

Conclusions:

  • TGNN offers a computationally efficient and accurate method for band gap prediction.
  • The developed model surpasses standard density functional theory calculations in accuracy.
  • This approach accelerates materials discovery by enabling rapid, reliable property prediction.