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Descubrimiento de aleaciones de alta entropía habilitadas para el aprendizaje automático

Ziyuan Rao¹, Po-Yen Tung^1,2, Ruiwen Xie³

¹Max-Planck-Institut für Eisenforschung GmbH, Düsseldorf, Germany.

Science (New York, N.Y.)

|October 6, 2022

Resumen

Este resumen es generado por máquina.

Desarrollamos una estrategia de aprendizaje activo para descubrir nuevas aleaciones Invar de alta entropía. Este enfoque identificó rápidamente dos aleaciones con una expansión térmica excepcionalmente baja, acelerando el descubrimiento de materiales.

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Área de la Ciencia:

Ciencias de los materiales
Trabajos de metalurgia
Ciencias de los materiales computacionales

Sus antecedentes:

Las aleaciones de alta entropía (HEAs) ofrecen propiedades únicas inaccesibles para los materiales convencionales.
El diseño de AES es un desafío debido a los vastos espacios de composición y las limitaciones de las reglas termodinámicas tradicionales.
El descubrimiento de HEAs con propiedades específicas a menudo se basa en la casualidad.

Objetivo del estudio:

Acelerar el diseño y el descubrimiento de aleaciones Invar de alta entropía.
Desarrollar una estrategia de aprendizaje activo para navegar por paisajes compositivos complejos.
Identificar las AES con coeficientes de expansión térmica excepcionalmente bajos.

Principales métodos:

Aprendizaje activo integrado con teoría funcional de densidad, cálculos termodinámicos y validación experimental.
Se empleó un enfoque de circuito cerrado para el diseño y la caracterización iterativa de materiales.
Examinó millones de composiciones potenciales utilizando el aprendizaje automático en datos escasos.

Principales resultados:

Identificó dos nuevas aleaciones de Invar de alta entropía.
Se obtienen coeficientes de expansión térmica extremadamente bajos (aprox. 2 × 10-6 K-1 en 300 K).
Demostró la eficacia de la estrategia de aprendizaje activo en un espacio de alta dimensión.

Conclusiones:

La estrategia de aprendizaje activo propuesta permite el descubrimiento rápido y automatizado de las AES.
Este enfoque es adecuado para optimizar las propiedades térmicas, magnéticas y eléctricas.
Las aleaciones identificadas representan avances significativos en los materiales de baja expansión térmica.