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Related Concept Videos

Energy Bands in Solids01:01

Energy Bands in Solids

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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.
 Band Formation:
When atoms are brought close together, as in a solid, these discrete energy levels begin to split due to the overlap of electron orbitals from adjacent atoms. This split occurs because of the Pauli exclusion principle, which states...
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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.
Conductor, Semiconductor,...
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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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Semiconductors01:22

Semiconductors

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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.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
1.2K
Fermi Level Dynamics01:12

Fermi Level Dynamics

528
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...
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Fermi Level01:18

Fermi Level

1.3K
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.
At absolute zero temperature, electrons fill all energy states up to the Fermi level, leaving upper states empty. As the temperature rises,...
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Related Experiment Video

Updated: Dec 8, 2025

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
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Quantum computation of silicon electronic band structure.

Frank T Cerasoli1, Kyle Sherbert1, Jagoda Sławińska1

  • 1Department of Physics, University of North Texas, Denton, TX 76203, USA. mbn@unt.edu.

Physical Chemistry Chemical Physics : PCCP
|September 23, 2020
PubMed
Summary

Researchers developed minimal depth quantum circuits to simulate periodic solids, successfully calculating silicon's band structure on a quantum computer. This advance paves the way for quantum materials simulation.

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Area of Science:

  • Quantum computing
  • Computational physics
  • Quantum chemistry

Background:

  • Hybrid classical-quantum algorithms offer potential for simulating fermionic systems beyond classical capabilities.
  • Minimal depth quantum circuits are crucial for accurately representing chemical systems.
  • Simulating periodic solids presents unique computational challenges.

Purpose of the Study:

  • To extend quantum chemistry methods for simulating molecules to calculating properties of periodic solids.
  • To demonstrate the first quantum computation of a solid-state band structure using minimal depth circuits.
  • To explore the potential of quantum computing for electronic structure calculations of quantum materials.

Main Methods:

  • Development of minimal depth quantum circuits tailored for periodic solids.
  • Implementation of the variational quantum eigensolver (VQE) algorithm.
  • Utilizing cloud-based quantum computing platforms for simulations.

Main Results:

  • Successfully computed the band structure of silicon using the VQE algorithm on a quantum machine.
  • Demonstrated the applicability of quantum chemistry simulation methods to periodic solid systems.
  • Achieved the first quantum computation of band structure for a solid material.

Conclusions:

  • Quantum chemistry methods can be extended to calculate properties of periodic solids.
  • Minimal depth quantum circuits and VQE are viable for solid-state simulations.
  • Quantum computing holds promise for advancing the study of quantum materials and electronic structure.