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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Alkenes undergo reduction by the addition of molecular hydrogen to give alkanes. Because the process generally occurs in the presence of a transition-metal catalyst, the reaction is called catalytic hydrogenation.
Metals like palladium, platinum, and nickel are commonly used in their solid forms — fine powder on an inert surface. As these catalysts remain insoluble in the reaction mixture, they are referred to as heterogeneous catalysts.
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Unlike the easy catalytic hydrogenation of an alkene double bond, hydrogenation of a benzene double bond under similar reaction conditions does not take place easily. For example, in the reduction of stilbene, the benzene ring remains unaffected while the alkene bond gets reduced. Hydrogenation of an alkene double bond is exothermic and a favorable process. In contrast, to hydrogenate the first unsaturated bond of benzene, an energy input is needed; that is, the process is endothermic. This is...
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Catalytic hydrogenation of alkenes is a transition-metal catalyzed reduction of the double bond using molecular hydrogen to give alkanes. The mode of hydrogen addition follows syn stereochemistry.
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If a set of reactants can yield multiple constitutional isomers, but one of the isomers is obtained as the major product, the reaction is said to be regioselective. In such reactions, bond formation or breaking is favored at one reaction site over others.
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Autoencoder variacional condicional para predecir condiciones adecuadas para reacciones de hidrogenación

Daniyar Mazitov1, Timur Gimadiev1,2, Assima Poyezzhayeva1,2

  • 1A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kremlevskaya Str. 18, 420008 Kazan, Russia.

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Un nuevo modelo de autoencoder variacional condicional (CVAE) predice con precisión las condiciones de reacción química, incluidos los catalizadores y la temperatura. Este enfoque generativo evita búsquedas complejas y ofrece flexibilidad y alto rendimiento para la planificación de la síntesis química.

Palabras clave:
reacciones mediadas por H2grafo de reacción condensadoautoencoder variacional condicionalreacciones de hidrogenaciónhidrogenólisispredicción de condiciones de reacción

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Área de la Ciencia:

  • Química Computacional
  • Aprendizaje Automático en Química
  • Química Sintética

Sus antecedentes:

  • La predicción precisa de las condiciones de reacción (RC) es vital para la planificación de la síntesis química.
  • Múltiples combinaciones de RC pueden producir resultados deseados, lo que complica la recomendación de condiciones.
  • Los métodos existentes a menudo requieren una enumeración exhaustiva o búsquedas combinatorias.

Objetivo del estudio:

  • Desarrollar un modelo generativo para predecir condiciones de reacción adecuadas.
  • Mejorar la flexibilidad y la precisión en la recomendación de condiciones de reacción.
  • Evitar la necesidad de búsquedas combinatorias de RCs potenciales.

Principales métodos:

  • Se empleó un modelo generativo de autoencoder variacional condicional (CVAE).
  • El CVAE se personalizó para generar diversos conjuntos de RCs válidos.
  • Se probaron tres variantes de distribución latente (Gaussiana, Flujo Normalizador Riemanniano, Uniforme Hiperesférico) para optimizar la precisión.
  • Los modelos se evaluaron en conjuntos de datos de diversos tamaños y complejidad.

Principales resultados:

  • El modelo CVAE predijo con éxito catalizadores, aditivos, temperatura y presión para reacciones de hidrogenación y mediadas por H2.
  • El CVAE Uniforme Hiperesférico (h-CVAE) demostró un rendimiento general y una precisión superiores para las predicciones top-k.
  • La evaluación comparativa confirmó el alto rendimiento de los modelos CVAE frente a los enfoques de última generación.
  • El modelo manejó conjuntos de datos con hasta ~7 × 10^42 combinaciones de condiciones potenciales.

Conclusiones:

  • Los modelos CVAE ofrecen un método flexible y preciso para predecir las condiciones de reacción química.
  • La variante h-CVAE es particularmente efectiva para aplicaciones que requieren alta precisión.
  • Este enfoque avanza significativamente la planificación automatizada de la síntesis química.