Computational chemistry has become an essential component of catalyst development and chemical engineering by providing detailed molecular-level insights into chemical reactions and material behavior. Advanced modeling techniques enable researchers to predict catalyst performance, optimize reaction mechanisms, and reduce the time and cost associated with experimental investigations. Computational tools are accelerating innovation across pharmaceuticals, energy, materials science, and industrial chemistry.
Recent developments in density functional theory (DFT), molecular dynamics simulations, quantum chemistry, and multiscale modeling have significantly enhanced the accuracy of computational predictions. Artificial intelligence and high-performance computing are further expanding the ability to screen thousands of catalyst candidates and simulate complex reaction environments with remarkable efficiency.
This session welcomes research on computational catalyst design, reaction mechanism analysis, molecular simulation, predictive modeling, and digital chemistry applications. Participants will explore how computational approaches complement experimental research and support the development of advanced catalytic technologies for future industrial applications.