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Electronic Structure of Atoms02:28

Electronic Structure of Atoms

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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...
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Fermi Level Dynamics01:12

Fermi Level Dynamics

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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.
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...
570
Electron Configurations02:46

Electron Configurations

24.8K
Electron configurations and orbital diagrams can be determined by applying the Aufbau principle (each added electron occupies the subshell of lowest energy available), Pauli exclusion principle (no two electrons can have the same set of four quantum numbers), and Hund’s rule of maximum multiplicity (whenever possible, electrons retain unpaired spins in degenerate orbitals).
The relative energies of the subshells determine the order in which atomic orbitals are filled (1s, 2s, 2p, 3s, 3p,...
24.8K
Fermi Level01:18

Fermi Level

1.4K
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,...
1.4K
Free Energy Changes for Nonstandard States03:25

Free Energy Changes for Nonstandard States

13.2K
The free energy change for a process taking place with reactants and products present under nonstandard conditions (pressures other than 1 bar; concentrations other than 1 M) is related to the standard free energy change according to this equation:
13.2K
Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

1.8K
NMR-active nuclei have energy levels called 'spin states' that are associated with the orientations of their nuclear magnetic moments. In the absence of a magnetic field, the nuclear magnetic moments are randomly oriented, and the spin states are degenerate. When an external magnetic field is applied, the spin states have only 2 + 1 orientations available to them. A proton with = ½ has two available orientations. Similarly, for a quadrupolar nucleus with a nuclear spin value of one, the...
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Related Experiment Video

Updated: Dec 21, 2025

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
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Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

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Fermionic neural-network states for ab-initio electronic structure.

Kenny Choo1, Antonio Mezzacapo2, Giuseppe Carleo3

  • 1Department of Physics, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland. kenny.choo@uzh.ch.

Nature Communications
|May 14, 2020
PubMed
Summary
This summary is machine-generated.

We developed a new neural-network quantum state method to accurately model interacting fermionic systems. This approach achieves chemical accuracy for electronic structure calculations, improving upon existing methods.

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Area of Science:

  • Quantum computing
  • Computational chemistry
  • Condensed matter physics

Background:

  • Neural-network quantum states (NQS) are powerful tools for simulating quantum systems.
  • Representing fermionic matter with NQS is an emerging research area.
  • Existing methods struggle with the complexity of interacting fermions.

Purpose of the Study:

  • To extend neural-network quantum states for modeling interacting fermionic problems.
  • To perform accurate electronic structure calculations for molecules.
  • To benchmark the new method against established computational chemistry techniques.

Main Methods:

  • Mapping fermionic degrees of freedom to spin ones, inspired by quantum simulation.
  • Utilizing neural-network quantum states for electronic structure calculations.
  • Benchmarking against coupled cluster methods and many-body variational states.

Main Results:

  • The proposed NQS extension successfully models interacting fermionic systems.
  • The method achieves chemical accuracy or better for several diatomic molecules.
  • Systematic improvements were observed compared to coupled cluster and Jastrow wave functions.

Conclusions:

  • The developed NQS approach offers a promising new avenue for fermionic simulations.
  • This method advances the application of neural networks in computational chemistry.
  • Future work will focus on further methodological improvements and broader applications.