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Escalar el aprendizaje profundo para el descubrimiento de materiales

Amil Merchant1, Simon Batzner2, Samuel S Schoenholz2

  • 1Google DeepMind, Mountain View, CA, USA. amilmerchant@google.com.

Nature
|November 29, 2023
PubMed
Resumen
Este resumen es generado por máquina.

El aprendizaje profundo con redes de gráficos acelera el descubrimiento de cristales inorgánicos diez veces, identificando 2.2 millones de nuevos materiales estables. Este avance amplía significativamente el panorama conocido de materiales estables para aplicaciones tecnológicas.

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Área de la Ciencia:

  • Ciencias de los materiales
  • Química computacional
  • Inteligencia artificial

Sus antecedentes:

  • El descubrimiento tradicional de cristales inorgánicos se basa en costosos métodos de prueba y error, lo que dificulta el rápido avance tecnológico.
  • Los modelos de aprendizaje profundo han demostrado un poder predictivo significativo en varios dominios científicos, lo que sugiere un potencial para aplicaciones de la ciencia de los materiales.

Objetivo del estudio:

  • Desarrollar y aplicar redes de gráficos a gran escala para mejorar significativamente la eficiencia y el alcance del descubrimiento de cristales inorgánicos.
  • Identificar nuevas estructuras cristalinas estables más allá de la intuición química humana y expandir el paisaje de materiales conocidos.

Principales métodos:

  • Red de gráficos de entrenamiento en un conjunto de datos de 48.000 cristales estables conocidos.
  • Utilizando el aprendizaje profundo a escala para predecir y descubrir nuevas estructuras cristalinas estables.
  • Realizar cientos de millones de cálculos de principios básicos para validar la estabilidad y las propiedades.

Principales resultados:

  • Logró una mejora de orden de magnitud en la eficiencia de descubrimiento de materiales.
  • Identificó 2,2 millones de nuevas estructuras cristalinas estables, muchas desconocidas anteriormente.
  • 736 de las estructuras estables descubiertas han sido validadas experimentalmente.
  • Desarrolló potenciales interatómicos aprendidos de alta precisión para simulaciones de dinámica molecular.

Conclusiones:

  • Las redes de gráficos a gran escala representan un cambio de paradigma en el descubrimiento de materiales, superando las limitaciones de los métodos tradicionales.
  • Los materiales descubiertos ofrecen un gran potencial para aplicaciones en energía limpia, procesamiento de información y más allá.
  • El marco computacional desarrollado y los materiales descubiertos aceleran los avances científicos y la innovación tecnológica.