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Aumentando los grandes modelos de lenguaje para el descubrimiento automatizado de extractantes de elementos F.

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Desarrollamos un flujo de trabajo de IA para diseñar ligandos selectivos para separar los elementos f, acelerando el descubrimiento. Este método identificó con éxito nuevos ligandos con alta selectividad Am(III) / Eu(III), superando los actuales parámetros de referencia.

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Área de la Ciencia:

  • Química Nuclear La Química Nuclear es el campo de la química nuclear.
  • Química computacional es la química computacional.
  • Ciencia de los materiales Ciencia de los materiales.

Sus antecedentes:

  • La separación eficiente de los elementos f (lantánidos y actinidos) es crucial para las tecnologías avanzadas, pero se ve obstaculizada por su similitud química.
  • El desarrollo de reactivos de extracción selectiva con disolventes para estos elementos es un proceso lento y difícil.

Objetivo del estudio:

  • Presentar un flujo de trabajo habilitado para IA para el diseño rápido y la detección computacional de ligandos de extractores selectivos para elementos f.
  • Acelerar el descubrimiento de nuevos ligandos con una mayor selectividad para separaciones difíciles como Am(III) / Eu(III).

Principales métodos:

  • Utilizó un modelo de lenguaje grande (SAFE-MolGen) para el diseño molecular guiado y la clasificación preliminar de ligandos.
  • Empleó un modelo de aprendizaje automático supervisado entrenado en datos experimentales para predecir el rendimiento del ligando en condiciones realistas.
  • Integró una tubería para construir complejos 3D de metal-ligando y realizar cálculos de energía libre mecánica cuántica para evaluar la selectividad.

Principales resultados:

  • Demostró el flujo de trabajo para las separaciones de Am (III) / Eu (III), un desafío crítico en la gestión de residuos nucleares.
  • Se identificaron varios ligandos de nuevo diseño que se prevé que ofrezcan una mayor selectividad Am(III) / Eu(III) en comparación con el extractante de referencia CyMe4BTBP.
  • Aceleró con éxito la exploración computacional del espacio molecular en este campo disperso de datos.

Conclusiones:

  • El flujo de trabajo habilitado para IA acelera significativamente la generación y evaluación de nuevos extractores de lantánidos y actinidos.
  • Este enfoque proporciona una estrategia general para el diseño de ligandos selectivos, superando las limitaciones de los métodos tradicionales.
  • Los ligandos desarrollados son prometedores para mejorar la eficiencia de las separaciones de elementos f en diversas aplicaciones tecnológicas.