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Estrategia de optimización de local a global asistida por aprendizaje automático para la predicción acelerada de

Gunjan R Ramteke1, K V Jovan Jose1

  • 1Advanced Artificial Intelligence Theoretical and Computational Chemistry Laboratory, School of Chemistry, University of Hyderabad, Hyderabad, Telangana 500046, India.

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Resumen

Desarrollamos LOGOS, una estrategia de aprendizaje automático, para predecir estructuras estables de clústeres moleculares. Este método acelera el descubrimiento de clústeres energéticamente favorables al aprender patrones de estructuras más pequeñas.

Palabras clave:
aprendizaje automáticopredicción de estructurasclústeres molecularesoptimizaciónquímica computacional

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Área de la Ciencia:

  • Química Computacional
  • Ciencia de Materiales
  • Física Química

Sus antecedentes:

  • La predicción de estructuras estables de clústeres moleculares requiere una gran cantidad de recursos computacionales.
  • La comprensión de los clústeres moleculares es crucial para diversos procesos químicos y físicos.

Objetivo del estudio:

  • Presentar una estrategia asistida por aprendizaje automático, LOGOS, para la predicción eficiente de estructuras estables de clústeres moleculares.
  • Acelerar la búsqueda de estructuras y la predicción de clústeres moleculares grandes.

Principales métodos:

  • Se desarrolló una estrategia de optimización de local a global (LOGOS) utilizando aprendizaje profundo.
  • Se identificaron patrones localizados y se predijeron sitios de unión para la construcción de clústeres.
  • Se generaron estructuras hijas optimizadas geométricamente en el espacio de características del potencial electrostático molecular (MESP).

Principales resultados:

  • Se evaluó LOGOS construyendo clústeres de estado fundamental de (CO2)n (n < 30).
  • Los resultados se corroboraron bien con las estructuras de energía mínima de la literatura.
  • Se demostró la predicción eficiente de clústeres hijos con un costo computacional mínimo.

Conclusiones:

  • LOGOS acelera la predicción de estructuras de clústeres moleculares grandes.
  • Proporciona una solución jerárquica práctica para identificar clústeres energéticamente favorables.
  • Aprovecha el aprendizaje profundo para comprender patrones complejos y predecir estructuras de manera eficiente.