Advances in computational chemistry and materials modeling are transforming the development of chemical processes and functional materials. We present industrially relevant case studies in catalysis, battery materials, corrosion inhibition, and semiconductor processing, where predictive modeling enables virtual screening, mechanistic understanding, and faster innovation. Using quantum chemistry, reactive and machine learned force fields, and kinetic Monte Carlo simulations, we compute properties such as reaction kinetics, degradation pathways, ion transport, adsorption energies, and surface reactivity to inform experimental design. These multiscale workflows—implemented in the Amsterdam Modeling Suite—have supported peer-reviewed publications and patents in collaboration with leading industrial R&D groups. This presentation highlights the growing impact of atomistic and multiscale modeling in accelerating time-to-market, reducing waste, and designing materials and processes for improved performance and sustainability. We also touch on the integration of machine learning and automated workflows to support decision-making for chemical engineers and materials scientists.
