Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Electron Orbital Model01:18

Electron Orbital Model

Orbitals are the areas outside of the atomic nucleus where electrons are most likely to reside. They are characterized by different energy levels, shapes, and three-dimensional orientations. The location of electrons is described most generally by a shell or principal energy level, then by a subshell within each shell, and finally, by individual orbitals found within the subshells.The first shell is closest to the nucleus, and it has only one subshell with a single spherical orbital called the...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Electronic Structure of Atoms02:28

Electronic Structure of Atoms


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 numbers:  n, l, ml, and...
Resonance and Hybrid Structures02:16

Resonance and Hybrid Structures

According to the theory of resonance, if two or more Lewis structures with the same arrangement of atoms can be written for a molecule, ion, or radical, the actual distribution of electrons is an average of that shown by the various Lewis structures.
Resonance Structures and Resonance Hybrids
The Lewis structure of a nitrite anion (NO2−) may actually be drawn in two different ways, distinguished by the locations of the N–O and N=O bonds.
Radicals: Electronic Structure and Geometry01:07

Radicals: Electronic Structure and Geometry

This lesson delves into the geometry of a radical, which is influenced by the electronic structure of the molecule. The principle is similar to that of a lone pair, where the unpaired electron influences the geometry at the radical center.
Accordingly, the structure of a trivalent radical lies between the geometries of carbocations and carbanions. An sp2-hybridized carbocation is trigonal planar, while an sp3-hybridized carbanion is trigonal pyramidal. Here, the difference in geometry is...
π Electron Effects on Chemical Shift: Overview01:27

π Electron Effects on Chemical Shift: Overview

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, resulting in...

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Dataset distillation for machine learning force field in phase transition regime.

The Journal of chemical physics·2026
Same author

DNA Nanotechnology-Enabled Precise Regulation of Nanozymes and Their Applications.

Research (Washington, D.C.)·2026
Same author

Low-Temperature Ethanol Gas Sensor Based on MoO<sub>3</sub>/Nb<sub>2</sub>C MXene Composite via Crystal Engineering and Facet Release.

Sensors (Basel, Switzerland)·2026
Same author

Saturated and Anisotropic Magnetostriction in an Altermagnet.

Journal of the American Chemical Society·2026
Same author

Continuous flow modular synthetic platform for accelerated drug discovery.

European journal of medicinal chemistry·2026
Same author

Occupation Dynamics of Floquet-Volkov States and Spectral Sum Rule.

Nano letters·2026
Same journal

The inequalities of GPU access.

Nature computational science·2026
Same journal

Social technologies need societal alignment.

Nature computational science·2026
Same journal

The Quantum Optimization Benchmarking Library.

Nature computational science·2026
Same journal

Setting benchmarks for practical quantum utility of combinatorial optimization.

Nature computational science·2026
Same journal

Evidence of scaling advantage on an NP-complete problem with enhanced quantum solvers.

Nature computational science·2026
Same journal

Leveraging longitudinal data to boost statistical power for gene-environment interaction analysis.

Nature computational science·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

Updated: Jun 12, 2026

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

Cálculos de estructura electrónica mediante aprendizaje profundo

Zechen Tang1, Haoxiang Chen2, Yang Li1

  • 1State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing, China.

Nature computational science
|December 22, 2025
PubMed
Resumen
Este resumen es generado por máquina.

El aprendizaje profundo está superando el compromiso entre precisión y eficiencia en los cálculos de estructura electrónica. Nuevos métodos como el Monte Carlo cuántico de aprendizaje profundo y la teoría de funcionales de densidad de aprendizaje profundo permiten simulaciones más complejas y a gran escala en ciencia de materiales y química.

Palabras clave:
aprendizaje profundoestructura electrónicaMonte Carlo cuánticoteoría de funcionales de densidadciencia de materialesquímica cuántica

Más Videos Relacionados

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
08:04

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

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

Published on: August 22, 2025

Videos de Experimentos Relacionados

Last Updated: Jun 12, 2026

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

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
08:04

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

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

Published on: August 22, 2025

Área de la Ciencia:

  • Física, Química y Ciencia de Materiales

Sus antecedentes:

  • Los cálculos de estructura electrónica de primeros principios son cruciales para el descubrimiento científico.
  • Su avance está limitado por el dilema de precisión-eficiencia.

Objetivo del estudio:

  • Destacar las metodologías de aprendizaje profundo que abordan el desafío de precisión-eficiencia en los cálculos de estructura electrónica.
  • Mostrar cómo estos métodos extienden las capacidades de las simulaciones cuánticas.

Principales métodos:

  • Monte Carlo cuántico de aprendizaje profundo para estudios precisos de electrones correlacionados.
  • Teoría de funcionales de densidad de aprendizaje profundo para simulaciones eficientes de materiales a gran escala.

Principales resultados:

  • Los avances en el aprendizaje profundo abordan el dilema de precisión-eficiencia.
  • Los nuevos métodos permiten cálculos de estructura electrónica precisos y eficientes.

Conclusiones:

  • El aprendizaje profundo mejora significativamente la escala y la complejidad de los cálculos de primeros principios.
  • Estos avances amplifican el impacto de la mecánica cuántica en el descubrimiento científico.