## 5 Computational Catalyst Design  
Computational chemistry is a branch that uses computer simulations to solve chemical problems, thereby bridging the gap between theoretical concepts and practical applications in chemical sciences [82; 83]. It employs theoretical methods and mathematical models to study the structures, properties, and behaviors of molecules and materials. This field is crucial for drug and materials design, engineering, and developing new technologies. Theoretical methods are essential for understanding molecular behavior, predicting chemical reactions, and designing new materials and drugs. Thus, they allow for the rational design of imprinting templates by predicting the most suitable functional monomers and their optimal configurations, which is particularly relevant for developing advanced materials, including ceramics with tailored properties. Researchers can simulate the target molecule-potential monomer interactions through computational chemistry techniques, such as molecular modeling and docking studies. Furthermore, molecular dynamics (MD) calculations can help in understanding how the template interacts with monomers at different stages of polymerization, and DFT (Density Functional Theory) calculations can assist in identifying the most energetically favorable interactions, which are critical for the specificity and affinity of the MIP. This computational strategy guides the selection of monomers that create stable complexes with the target, thereby ensuring high specificity and binding affinity in the final MIP. The investigation of functional ceramic materials in molecular imprinting systems can also benefit from these computational approaches, enabling the optimization of their properties. Notably, DFT modeling can provide insights into selectivity tuning by evaluating interactions between imprinted sites and the target molecule under various conditions. This aids in selecting monomers that form stable complexes with the target, thereby ensuring high specificity and binding affinity in the final MIP [132; 133; 134; 135]. Figure 4A illustrates the principle of computational catalyst design strategies, while Figure 4B reveals the chemical structure of a functionalized monomer, the SiO\({}_{2}\) ceramic matrix, and the calculated adsorption energy. This computational approach enables the optimization of MIP selectivity and affinity, facilitating the rational catalyst design for applications in sensing, catalysis, and bioactive compound separation. It has been reported that the conditions under which MIPs are synthesized, including temperature, solvent, and initiator concentration, can significantly impact the polymer's properties. Hence, from this perspective, theoretical approaches are crucial for optimizing these parameters to enhance imprinting efficiency and provide thermodynamic and kinetic insights into the process, thereby guiding synthesis [136]. By simulating various scenarios, researchers can identify the best conditions that lead to forming well-defined recognition sites. Additionally, DFT-based calculations can predict active site locations and interaction geometries, i.e., contributing to a deeper mechanistic understanding of the binding process. This knowledge enables the fine-tuning of MIP design to achieve desired selectivity and sensitivity. Therefore, performance prediction is crucial in MIP development, with theoretical models based on Quantitative Structure-Activity Relationship (QSAR) correlating structural features of MIPs with their binding affinities and selectivity toward guiding the design of more effective polymers [137; 138; 139; 63]. Figure 4: (**A**) An overview of computational catalyst design strategies. (**B**) A computational model of MIPs interacting with SiO\({}_{2}\) (silicon dioxide) ceramic materials, calculated via DFT. Thus, a study developed by [PERSON] et al.