Sandeep Rai,
- DPC Member, Department of Chemistry, UPL University of Sustainable Technology, Block No. 402, Ankleshwar-Valia Road, Taluka: Valia, Dist.: Bharuch, Gujarat, India
Abstract
Computational catalysis has emerged as a transformative scientific discipline that integrates quantum chemistry, molecular modeling, machine learning, and density functional theory (DFT) to understand catalytic mechanisms and design highly efficient catalytic systems for sustainable industrial applications. The increasing global demand for environmentally responsible chemical manufacturing has accelerated research on advanced catalytic materials including transition metal catalysts, metal–organic frameworks (MOFs), homogeneous catalysts, heterogeneous systems, and bimetallic catalysts involving nickel and iron. These catalytic systems play a significant role in hydrogenation, methanation, carbon dioxide conversion, and carbon–carbon bond formation reactions. Computational chemistry provides atomistic-level insights into adsorption behavior, electronic structures, catalyst stability, and activation energy barriers, thereby enabling rational catalyst optimization before experimental synthesis. Recent computational studies have provided detailed mechanistic understanding of elementary reaction steps such as oxidative addition, transmetallation, ligand exchange, and reductive elimination in cross-coupling chemistry. DFT-based approaches have enabled accurate prediction of catalytic activity, reaction energetics, scaling relations, adsorption energies, and volcano plots for methanation reactions and catalytic carbon–carbon coupling processes. Machine learning and artificial intelligence have further accelerated catalyst discovery by identifying nonlinear relationships between catalyst descriptors and catalytic performance, thereby reducing the need for extensive trial-and-error experimentation. In parallel, biocatalytic transformations using immobilized enzymes, engineered proteins, and nanobiocatalysts have gained increasing importance for environmentally friendly synthesis under mild operating conditions. Nanostructured supports, multifunctional hybrid catalytic systems, and enzyme immobilization technologies have improved catalytic stability, selectivity, and reusability for industrial biotechnology applications. This paper discusses recent advances in computational catalysis and biocatalytic transformation with emphasis on mechanistic understanding, catalyst engineering, green nanomaterials, process intensification, and sustainable industrial applications in energy, pharmaceuticals, environmental remediation, and fine chemical synthesis.
Keywords: Computational catalysis, Density functional theory, Machine learning, Nickel catalyst, Iron catalyst, Methanation, Nanobiocatalyst, Enzyme immobilization, Green chemistry, Transition states
[This article belongs to Journal of Catalyst & Catalysis ]
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Journal of Catalyst & Catalysis
| Volume | 13 | |
| Issue | 02 | |
| Received | 02/06/2026 | |
| Accepted | 11/06/2026 | |
| Published | 25/06/2026 | |
| Publication Time | 23 Days |