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

Third Law of Thermodynamics02:38

Third Law of Thermodynamics

22.1K
A pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
22.1K
Second Law of Thermodynamics02:49

Second Law of Thermodynamics

27.1K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
27.1K
Second Law of Thermodynamics00:53

Second Law of Thermodynamics

68.7K
The Second Law of Thermodynamics states that entropy, or the amount of disorder in a system, increases each time energy is transferred or transformed. Each energy transfer results in a certain amount of energy that is lost—usually in the form of heat—that increases the disorder of the surroundings. This can also be demonstrated in a classic food web. Herbivores harvest chemical energy from plants and release heat and carbon dioxide into the environment. Carnivores harvest the...
68.7K
First Law of Thermodynamics00:37

First Law of Thermodynamics

80.9K
The First Law of Thermodynamics states that energy cannot be created or destroyed, only transformed. This can be demonstrated within a classic food web where light energy from the sun is harnessed as radiant energy by plants, converted into chemical energy, and stored as complex carbohydrates. The vegetation is then consumed by animals and during the digestion process, the sugars release energy as heat. The sugars also produce chemical energy that either gets used up doing work, stored in...
80.9K
First Law of Thermodynamics02:16

First Law of Thermodynamics

41.2K
Energy Conservation
41.2K
Classifying Matter by Composition03:35

Classifying Matter by Composition

91.0K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
91.0K

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Efgartigimod as a Steroid-sparing Therapy for Evans Syndrome: A Case Report.

Internal medicine (Tokyo, Japan)·2026
Same author

Zn-Assisted Mg Ion Transport in Spinel Oxide Cathodes: Insights From Neural Network Simulations.

Chemistry, an Asian journal·2025
Same author

Detection of three novel loci involved in reduced seed-shattering behaviour of an aus rice cultivar, Oryza sativa Kasalath.

Molecular genetics and genomics : MGG·2025
Same author

Causal Relationship between Potential Shift and Molecular Structure in Concentrated Electrolytes.

The journal of physical chemistry. B·2025
Same author

Revisiting the ion dynamics in Li <sub><i>x</i></sub> CoO<sub>2</sub> and Na <sub><i>x</i></sub> CoO<sub>2</sub>.

Chemical science·2025
Same author

Electrically pumped laser oscillation of C-band InAs quantum dot vertical-cavity surface-emitting lasers on InP(311)B substrate.

Optics express·2025

Video Experimental Relacionado

Updated: Feb 11, 2026

Reconstituting and Characterizing Actin-Microtubule Composites with Tunable Motor-Driven Dynamics and Mechanics
09:10

Reconstituting and Characterizing Actin-Microtubule Composites with Tunable Motor-Driven Dynamics and Mechanics

Published on: August 25, 2022

3.8K

Detección de anomalías impulsada por el cribado de la estabilidad termodinámica a partir de descriptores de

Keisuke Makino1, Yudai Yamaguchi1, Naoto Tanibata1

  • 1Department of Advanced Ceramics, Nagoya Institute of Technology, Nagoya, Aichi 466-8555, Japan.

The journal of physical chemistry letters
|February 10, 2026
PubMed
Resumen

Este estudio introduce un detector de anomalías de autoencoder utilizando datos de composición para predecir la sintetizabilidad del material. El modelo identifica materiales inestables y descubre factores que influyen en su formación, como pares de elementos y equilibrio de carga.

Más Videos Relacionados

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
09:41

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Published on: May 29, 2018

10.0K
How to Stabilize Protein: Stability Screens for Thermal Shift Assays and Nano Differential Scanning Fluorimetry in the Virus-X Project
07:22

How to Stabilize Protein: Stability Screens for Thermal Shift Assays and Nano Differential Scanning Fluorimetry in the Virus-X Project

Published on: February 11, 2019

29.3K

Videos de Experimentos Relacionados

Last Updated: Feb 11, 2026

Reconstituting and Characterizing Actin-Microtubule Composites with Tunable Motor-Driven Dynamics and Mechanics
09:10

Reconstituting and Characterizing Actin-Microtubule Composites with Tunable Motor-Driven Dynamics and Mechanics

Published on: August 25, 2022

3.8K
Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
09:41

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Published on: May 29, 2018

10.0K
How to Stabilize Protein: Stability Screens for Thermal Shift Assays and Nano Differential Scanning Fluorimetry in the Virus-X Project
07:22

How to Stabilize Protein: Stability Screens for Thermal Shift Assays and Nano Differential Scanning Fluorimetry in the Virus-X Project

Published on: February 11, 2019

29.3K

Área de la Ciencia:

  • Ciencia de los materiales Ciencia de los materiales.
  • Materiales computacionales Ciencia de la ciencia.
  • Aprendizaje automático en materiales.

Sus antecedentes:

  • La informática de materiales a menudo pasa por alto nuevas composiciones que no están presentes en las bases de datos existentes.
  • El descubrimiento de nuevos materiales requiere métodos eficientes para explorar vastos espacios de composición.

Objetivo del estudio:

  • Desarrollar un modelo de detección de anomalías basado en la composición para predecir la sintetizabilidad del material.
  • Identificar las características clave que influyen en la estabilidad y la formación potencial de nuevos materiales.

Principales métodos:

  • Se entrenó un modelo de autoencoder en compuestos inorgánicos estables y casi estables de la base de datos del Proyecto de Materiales.
  • El error de reconstrucción (error de la raíz media cuadrada - RMSE) se utilizó como una puntuación de anomalía.
  • El análisis de la importancia de las características se realizó en pares de elementos y sus configuraciones electrónicas (producto spdf).

Principales resultados:

  • El RMSE del modelo se correlacionó con la inestabilidad termodinámica (energía por encima del casco).
  • Las desviaciones de la neutralidad de carga en óxidos ficticios aumentaron el RMSE, incluso sin información explícita de carga.
  • Los pares de elementos, particularmente su producto spdf, fueron predictores clave de RMSE, con algunos pares como el Tantalum (Ta) que muestran desviaciones consistentes.

Conclusiones:

  • Los autoencoders de solo composición pueden predecir la sintetizabilidad del material e identificar posibles inestabilidades.
  • Las propiedades de los pares de elementos y las configuraciones electrónicas influyen significativamente en la formación del material.
  • El modelo proporciona un marco para explorar composiciones no registradas en informática de materiales.