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Related Concept Videos

Types of Semiconductors01:20

Types of Semiconductors

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Intrinsic semiconductors are highly pure materials with no impurities. At absolute zero, these semiconductors behave as perfect insulators because all the valence electrons are bound, and the conduction band is empty, disallowing electrical conduction. The Fermi level is a concept used to describe the probability of occupancy of energy levels by electrons at thermal equilibrium. In intrinsic semiconductors, the Fermi level is positioned at the midpoint of the energy gap at absolute zero. When...
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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.
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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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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.
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Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model.

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We developed a unified deep-learning interatomic potential (DPA-Semi) for 19 semiconductors. This model achieves high accuracy, paving the way for universal interatomic potentials in materials science.

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Area of Science:

  • Computational Materials Science
  • Machine Learning in Physics
  • Semiconductor Physics

Background:

  • Machine-learning interatomic potentials offer ab initio accuracy for large-scale simulations.
  • A universal interatomic model applicable to diverse semiconductors without parameter tuning is needed.

Purpose of the Study:

  • To develop a unified deep-learning interatomic potential (DPA-Semi) for 19 semiconductors.
  • To compare the performance of the unified model against individual models for each semiconductor.
  • To assess the accuracy of the DPA-Semi model against established computational methods.

Main Methods:

  • Density functional theory (DFT) calculations using numerical atomic orbitals basis sets for training data generation.
  • Development of a unified deep-learning interatomic potential (DPA-Semi) model.
  • Systematic comparison of solid and liquid phase properties across different machine-learning models.

Main Results:

  • The DPA-Semi model was developed for 19 semiconductors (group IIB to VIA).
  • Independent deep potential models were created for detailed comparative analysis.
  • The DPA-Semi model demonstrated accuracy comparable to GGA exchange-correlation functional quality.

Conclusions:

  • The DPA-Semi model serves as a pretrained model for a universal interatomic potential.
  • It shows significant potential for studying a wide range of group IIB to VIA semiconductors.
  • The model advances the development of accurate and broadly applicable interatomic potentials in materials simulations.