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El aprendizaje automático acelera el descubrimiento de nuevos materiales súper duros. Los investigadores sintetizaron un carburo de tungsteno de renio y un borocarburo de tungsteno de molibdeno con una dureza excepcional superior a 40 GPa.

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Área de la Ciencia:

  • Ciencias de los materiales
  • Ciencias de los materiales computacionales
  • Química del estado sólido

Sus antecedentes:

  • El desarrollo de materiales con propiedades mecánicas excepcionales, particularmente de alta dureza, es un desafío importante.
  • El modelado predictivo puede acelerar el descubrimiento de nuevos materiales inorgánicos funcionales.

Objetivo del estudio:

  • Desarrollar un modelo de aprendizaje automático para predecir módulos elásticos como un proxy para la dureza del material.
  • Identificar y sintetizar nuevos compuestos inorgánicos ultraincompresibles y superduros.

Principales métodos:

  • Se empleó un modelo de regresión de máquina vectorial de soporte para examinar 118.287 compuestos de bases de datos de estructura cristalina.
  • El carburo de tungsteno de renio ternario y el borocarburo de tungsteno de molibdeno cuaternario se sintetizaron a presión ambiente.
  • Se realizaron mediciones de celdas de yunque de diamante a alta presión y pruebas de microhardura de Vickers.

Principales resultados:

  • El aprendizaje automático predijo con precisión el módulo de masa de los compuestos sintetizados con menos del 10% de error.
  • Ambos compuestos sintetizados exhibieron un comportamiento ultraincompresible y una dureza extremadamente alta (> 40 GPa).
  • Los materiales identificados superaron el umbral superduro a bajas cargas de hendidura.

Conclusiones:

  • El aprendizaje automático es una estrategia efectiva para acelerar el descubrimiento de materiales inorgánicos funcionales avanzados.
  • El modelo desarrollado identificó con éxito nuevos compuestos súper duros con propiedades mecánicas excepcionales.
  • Este trabajo demuestra un enfoque poderoso para el diseño y la síntesis de materiales específicos.