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

Network Covalent Solids02:18

Network Covalent Solids

14.5K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
14.5K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.9K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.9K
Metallic Solids02:37

Metallic Solids

18.7K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
18.7K
Metal-Ligand Bonds02:51

Metal-Ligand Bonds

21.5K
The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
21.5K
Structural Isomerism02:34

Structural Isomerism

19.7K
Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula. Structural isomerism of coordination compounds can be divided into two subcategories, the linkage isomers and coordination-sphere isomers.
Linkage isomers occur when the coordination compound contains a ligand that can bind to the transition metal center through two different atoms. For example, the CN− ligand can bind through the carbon atom or through the nitrogen atom. Similarly, SCN− can...
19.7K
Properties of Transition Metals02:58

Properties of Transition Metals

27.1K
Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
27.1K

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Optimization of print parameters for batch and continuous manufacturing of three-dimensional (3D) printed dosage forms using artificial intelligence and machine learning.

Drug delivery and translational research·2025
Same author

Prediction of Solute Segregation at Metal/Oxide Interfaces Using Machine Learning Approaches.

Molecules (Basel, Switzerland)·2025
Same author

Do Orthopedic Surgery Residency Program Web Sites Address Diversity and Inclusion?

HSS journal : the musculoskeletal journal of Hospital for Special Surgery·2022
Same author

Role of Chemistry and Crystal Structure on the Electronic Defect States in Cs-Based Halide Perovskites.

Materials (Basel, Switzerland)·2021
Same author

Evaluation of Tellurium as a Fuel Additive in Neodymium-Containing U-Zr Metallic Fuel.

Scientific reports·2019
Same author

Impact of iodine antisite (I<sub>Pb</sub>) defects on the electronic properties of the (110) CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> surface.

The Journal of chemical physics·2018

Video Experimental Relacionado

Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

570

Enlace estructura-propiedad en aleaciones utilizando red neuronal de gráficos y inteligencia artificial explicable

Benjamin Rhoads1, Abigail Hogue1, Lars Kotthoff2

  • 1Department of Mechanical Engineering, University of Mississippi, University, MS 38677, USA.

Materials (Basel, Switzerland)
|August 28, 2025
PubMed
Resumen

Las redes neuronales de gráficos (GNNs) predicen eficientemente las propiedades mecánicas de las aleaciones de Ni-Al a partir de microestructuras, superando a las redes neuronales de convolución (CNNs). Las GNN ofrecen información interpretable y requieren menos potencia computacional para aplicaciones de ciencia de materiales.

Palabras clave:
aprendizaje profundored neuronal gráficaAprendizaje automáticoAcero y sus derivadosSimulación de campo de faseEstructura vínculo de propiedad

Más Videos Relacionados

Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures
09:12

Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures

Published on: August 10, 2017

7.7K
Designing Silk-silk Protein Alloy Materials for Biomedical Applications
11:14

Designing Silk-silk Protein Alloy Materials for Biomedical Applications

Published on: August 13, 2014

18.5K

Videos de Experimentos Relacionados

Last Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

570
Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures
09:12

Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures

Published on: August 10, 2017

7.7K
Designing Silk-silk Protein Alloy Materials for Biomedical Applications
11:14

Designing Silk-silk Protein Alloy Materials for Biomedical Applications

Published on: August 13, 2014

18.5K

Área de la Ciencia:

  • Ciencias de los materiales
  • Ciencias de los materiales computacionales
  • Inteligencia artificial en los materiales

Sus antecedentes:

  • El aprendizaje profundo acelera la ciencia de los materiales al predecir las relaciones microestructura-propiedad.
  • Las redes neuronales de convolución (CNN) pueden analizar microestructuras 3D, pero exigen recursos extensos.
  • Existen limitaciones en las CNNs con respecto al tamaño de la red y el tiempo de entrenamiento para análisis de materiales complejos.

Objetivo del estudio:

  • Entrenar y evaluar una red neuronal de gráficos (GNN) para predecir las propiedades mecánicas de las aleaciones de Ni-Al.
  • Comparar la eficiencia y la interpretabilidad de las GNN con las CNN para el análisis de la microestructura.
  • Aprovechar la IA explicable para una comprensión más profunda de la predicción de propiedades materiales.

Principales métodos:

  • Microestructuras de aleación de Ni-Al generadas mediante el modelado de campo de fase.
  • Entrenó una red neuronal de gráficos (GNN) en estas microestructuras para predecir la evolución de las propiedades mecánicas.
  • Se utilizó el análisis de saliencia y la inferencia bayesiana para la interpretabilidad del modelo y la determinación de parámetros.

Principales resultados:

  • El GNN predijo con precisión el fortalecimiento de la aleación a través de diferentes tamaños y dimensiones de la microestructura.
  • GNN demostró un rendimiento superior en comparación con las CNN, que requieren menos utilización de la GPU.
  • Las herramientas de IA explicables proporcionaron información interpretable sobre las predicciones de GNN.

Conclusiones:

  • Las GNN ofrecen un método preciso, eficiente e interpretable para extraer información de las microestructuras de los materiales.
  • Las GNN superan las limitaciones de las CNN con respecto al tamaño de la microestructura y las restricciones dimensionales.
  • Este enfoque avanza en la predicción de las propiedades de los materiales y mejora la comprensión a través de la IA explicable.