ATOMISTIC MODELING AND AI FOR MATERIALS DESIGN

Credit Hours: 3 · Graduate

Advances in first-principles simulation, high-throughput computation, and artificial intelligence have transformed modern materials science. This course introduces the methods and workflows used in computational materials design and discovery, with emphasis on density functional theory, materials databases, automated workflows, machine learning, machine-learned interatomic potentials, and emerging generative AI approaches. Students will gain practical experience using modern computational tools to set up, run, and interpret simulations and data-driven models for materials problems. Hands-on exercises using state-of-the-art codes, literature discussions, and a final project will reinforce the major concepts and applications.