This course explores the rapidly evolving field of computational chemistry at the intersection of quantum mechanics and artificial intelligence. Students will build a solid foundation in density functional theory (DFT) and ab initio molecular dynamics (AIMD), while examining how machine learning (ML) is transforming multiscale simulations. DFT calculations will be performed using ORCA, and molecular dynamics simulations will be conducted with Quantum ESPRESSO. The ML component will be implemented in Python, with an emphasis on PyTorch, covering techniques such as linear regression, neural network potentials, and physics-informed neural networks (PINNs). The course blends theoretical instruction with practical, hands-on labs, enabling students to simulate molecular systems and materials using both traditional and ML-based approaches. Real-world applications in materials discovery, catalysis, and biophysics will be highlighted. By the end of the course, students will be able to critically assess, apply, and integrate advanced computational tools for research in nanoscale science.
Please remember that the Undergraduate Calendar is always the official source for all course descriptions.