Tesis doctoralsDepartament de Química

More is Different: Modern Computational Modeling for Heterogeneous Catalysis

  • Datos identificativos

    Identificador:  TDX:3909
    Autores:  García Carrillo, Sergio Pablo
    Resumen:
    The combination of Experimental observations and Density Functional Theory studies is one of the pillars of modern chemical research. As they enable the collection of additional physical information of a chemical system, hardly accessible via the experimental setting, Density Functional Theory studies are widely employed to model and predict the behavior of a diverse variety of chemical compounds under unique environments. Particularly, in heterogeneous catalysis, Density Functional Theory models are commonly employed to evaluate the interaction between molecular compounds and catalysts, lately linking these interpretations with experimental results. However, high complexity found in both, catalytic settings and reactivity, implies the need of sophisticated methodologies involving automation, storage and analysis to correctly study these systems. Here, I present the development and combination of multiple methodologies, aiming at correctly asses complexity. Also, this work shows how the provided techniques have been actively used to study novel catalytic settings of academic and industrial interest.
  • Otros:

    Editor: Universitat Rovira i Virgili
    Fecha: 2022-07-11, 2022-07-20T12:05:25Z, 2022-07-20T12:05:25Z
    Identificador: http://hdl.handle.net/10803/674854
    Departamento/Instituto: Departament de Química Analítica i Química Orgànica, Universitat Rovira i Virgili.
    Idioma: eng
    Autor: García Carrillo, Sergio Pablo
    Director: López Alonso, Núria
    Fuente: TDX (Tesis Doctorals en Xarxa)
    Formato: application/pdf, application/pdf, 277 p.
  • Palabras clave:

    Automation
    Machine Learning
    Heterogeneous Catalysis
    Automatización
    Aprendizaje automático
    Catálisis heterogénea
    Automatització
    Aprenentatge automàtic
    Catàlisi heterogènia
    519.1
    Ciències
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