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Estructuras de granate de alta entropía desordenadas por cationes como electrolitos de estado sólido para baterías de

Jiwon Sun1,2, JunHo Song1, Juo Kim1,3

  • 1School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.

ACS applied materials & interfaces
|August 25, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un marco de aprendizaje automático para seleccionar rápidamente electrolitos de estado sólido de alta entropía para baterías de estado sólido. El método identifica materiales prometedores del tipo granate con alta conductividad iónica para baterías más seguras y de alta energía.

Palabras clave:
estructuras de granate de alta entropíaAprendizaje automáticoAprendizaje automático potencial interatómicoDinámica molecularelectrolitos en estado sólido

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Área de la Ciencia:

  • Ciencias de los materiales
  • La electroquímica
  • Química computacional

Sus antecedentes:

  • Los electrolitos de estado sólido de alta entropía (HE SSEs) ofrecen un mayor rendimiento y seguridad para las baterías de estado sólido (ASSB).
  • El vasto espacio químico y la complejidad de las ETSE desafían los métodos de detección experimentales y computacionales tradicionales.
  • Acelerar el descubrimiento de nuevas EES de alta tecnología es crucial para el avance de la tecnología de las baterías.

Objetivo del estudio:

  • Desarrollar y aplicar una nueva metodología basada en el aprendizaje automático (ML) para el cribado eficiente de candidatos de granada de alta entropía con desorden catiónico (CDHE).
  • Acelerar la exploración de las SSE de educación superior prometedoras con un coste computacional reducido y una eficiencia mejorada.
  • Identificar nuevos materiales tipo granate CDHE con una alta conductividad iónica para ASSB.

Principales métodos:

  • Se utilizó un modelo sustituto basado en ML para seleccionar 4348 candidatos a SSE tipo granate CDHE basados en la conductividad electrónica y la estabilidad termodinámica.
  • Se utiliza una red neuronal de gráficos hamiltonianos de cristal (CHGNet) para determinar configuraciones atómicas estables y calcular las propiedades elásticas para la supresión de la dendrita.
  • Se realizaron simulaciones de dinámica molecular (DM) con un potencial CHGNet afinado para evaluar la difusión del litio y la conductividad iónica.

Principales resultados:

  • Se ha seleccionado con éxito un gran conjunto de datos de candidatos a SSE de tipo granate CDHE, filtrando la conductividad electrónica y la favorabilidad termodinámica.
  • Se han identificado materiales candidatos con propiedades elásticas favorables, lo que indica su potencial para suprimir la formación de dendritas y garantizar la estabilidad interfacial.
  • Se han confirmado tres candidatos prometedores de tipo granate CDHE que exhiben conductividades iónicas superiores a 10−4 S/cm a temperatura ambiente mediante simulaciones de MD.

Conclusiones:

  • El marco de cribado basado en el aprendizaje automático desarrollado acelera significativamente el descubrimiento de SSE de alto rendimiento.
  • Los materiales identificados de tipo granate CDHE demuestran el potencial para las baterías de estado sólido de próxima generación.
  • Este enfoque ofrece una vía computacionalmente eficiente para explorar sistemas de materiales complejos para aplicaciones de almacenamiento de energía.