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Flujo de trabajo humano-IA guiado por químico para síntesis de marco orgánico covalente.
Lihan Chen1, Zhen Lu2, Lin Chen2
1College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China.
Journal of the American Chemical Society
|February 11, 2026
Resumen
Este estudio introduce un flujo de trabajo humano-IA para optimizar la síntesis del marco orgánico covalente (COF), yendo más allá del ensayo y error. El sistema de IA mejora la precisión de la predicción para la selección de disolventes y catalizadores en el diseño de materiales COF.
Área de la Ciencia:
- Ciencia de los materiales Ciencia de los materiales.
- La inteligencia artificial es inteligencia artificial.
- Síntesis Química La síntesis química es el proceso de síntesis química.
Sus antecedentes:
- La síntesis del marco orgánico covalente (COF) se basa en gran medida en la intuición de los expertos y la experimentación iterativa, lo que plantea desafíos para la escalabilidad y la reproducibilidad.
- Los métodos actuales carecen de enfoques sistemáticos para el diseño racional, lo que dificulta el descubrimiento eficiente de nuevos materiales COF.
Objetivo del estudio:
- Desarrollar un flujo de trabajo humano-IA guiado por químicos para digitalizar y optimizar el circuito de razonamiento de síntesis de COF.
- Mejorar la predicción de las condiciones óptimas de síntesis (solvente, catalizador, temperatura, tiempo, estequiometría) para los COF.
- Para permitir el diseño racional y la síntesis escalable de materiales reticulares complejos.
Principales métodos:
- Construyó una base de conocimiento de síntesis de COF a partir de más de 800 publicaciones (2709 protocolos).
- Acoplado a una base de conocimiento de literatura estructurada con modelos de lenguaje de gran tamaño aumentados por recuperación para la generación de hipótesis.
- Integró un módulo de diagnóstico consciente del experimento utilizando observaciones macroscópicas y difracción de rayos X en polvo (PXRD) para actualizaciones iterativas.
Principales resultados:
- El flujo de trabajo humano-IA demostró tasas de éxito mejoradas de disolvente-catalizador de hasta 0,83 en puntos de referencia de dejar una salida en 60 COFs mantenidos.
- Sintetizó con éxito dos COF fluorados (TAPPy-4F y TAPPy-8F) que exhiben cristalinidad y porosidad permanente.
- El sistema mostró una sólida transferibilidad más allá de los estudios de casos individuales, validando su generalizabilidad.
Conclusiones:
- El flujo de trabajo humano-IA desarrollado ofrece un paradigma generalizable y escalable para el diseño racional de COFs.
- Este enfoque integra el conocimiento experto con la exploración basada en modelos, acelerando el descubrimiento de materiales.
- El sistema simula efectivamente el circuito de razonamiento del químico, mejorando la eficiencia y la reproducibilidad de la síntesis.
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