Computational Modeling of Molecular Interactions in Catalysis

Abstract

Catalysis plays a critical role in accelerating chemical reactions, impacting industries ranging from pharmaceuticals to energy production. Understanding molecular interactions in catalytic systems is essential for designing efficient and selective catalysts. Computational modeling offers powerful tools to probe these interactions at the atomic and molecular levels, providing insights that complement experimental approaches. This paper presents a detailed study of computational methods applied to molecular interactions in catalysis, focusing on density functional theory (DFT), molecular dynamics (MD), and hybrid quantum mechanics/molecular mechanics (QM/MM) simulations. The study systematically explores how these techniques model adsorption, reaction mechanisms, and transition states on catalytic surfaces and active sites. Through a combination of static and dynamic simulations, we analyze key molecular parameters such as binding energies, activation barriers, and charge distributions, highlighting their influence on catalytic efficiency. Benchmark cases including metal nanoparticle catalysis, enzyme catalysis, and heterogeneous catalysis are examined. Results demonstrate the ability of computational models to predict catalytic behavior, enabling the rational design of catalysts with improved performance. Challenges such as computational cost, accuracy of exchange-correlation functionals, and treatment of solvent effects are discussed. The paper also addresses recent advances in machine learningassisted modeling that accelerate catalyst discovery. Overall, this research underscores the importance of computational approaches in elucidating molecular-level phenomena in catalysis and provides a framework for integrating simulations with experimental studies to optimize catalytic systems.