The Role of Quantum Chemistry in Catalyst Design for Chemical Technologies and Nanoengineering

Abstract

Quantum chemistry has become one of the central intellectual and practical tools in modern catalyst design because it links atomic-scale electronic structure to measurable catalytic performance. In chemical technologies and nanoengineering, where catalyst behavior is governed by subtle changes in composition, coordination environment, morphology, defect structure, support interactions, and operating conditions, quantum-chemical methods provide a framework for rationalizing activity, selectivity, and stability before large-scale experimental screening is undertaken. The emergence of density functional theory (DFT), post-Hartree–Fock methods for benchmark calculations, ab initio molecular dynamics, embedded quantum mechanics/molecular mechanics approaches, and more recently machine learning integrated with quantum chemistry has transformed catalyst discovery from a largely empirical enterprise into a predictive discipline. These methods are now routinely used to identify active sites, map reaction pathways, quantify adsorption energetics, interpret spectroscopic signatures, construct scaling relations, and reveal the origin of structure–property relationships in homogeneous, heterogeneous, electrochemical, photocatalytic, and single-atom catalytic systems (Nørskov et al., 2009; Seh et al., 2017; Sankar et al., 2020; Casillo et al., 2025).

In nanoengineered systems, the importance of quantum chemistry is even more pronounced. Nanocatalysts expose a high density of undercoordinated atoms, strain fields, interfaces, ligands, and support-mediated electronic effects that are difficult to isolate experimentally but can be examined directly through theory. Quantum-chemical analysis helps distinguish geometric effects from electronic effects, predict the consequences of size and shape changes, and guide the stabilization of metastable yet highly active structures such as single atoms, subnanometer clusters, defect-rich oxides, and heterostructured interfaces (Negreiros et al., 2012; Cao et al., 2016; Kraushofer et al., 2022). At the same time, the field faces important challenges related to functional accuracy, dynamic restructuring, solvation, electric fields, entropy, and the complexity gap between model catalysts and working materials. This review examines the conceptual foundations, methodological developments, and practical applications of quantum chemistry in catalyst design, with special emphasis on its role in emerging chemical technologies and nanoengineering. The review also outlines current limitations and future directions, arguing that the next phase of catalyst innovation will rely on tighter integration between quantum chemistry, operando characterization, data science, and autonomous experimentation (Lewis et al., 2023; Du et al., 2024; Shao et al., 2025).