Machine Learning and AI in Energy Applications

Recent developments in Machine Learning (ML) and Artificial Intelligence (AI) have revolutionized how we approach science and engineering. ML and AI have accelerated the discovery of new materials for catalysis and applications in solar or nuclear energy. They have enabled the high-throughput screening of nonporous materials for sustainable energy solutions. This course will provide a practical introduction to the machine learning concepts, methods, and tools to STEM students, including regression models, neural networks, modern deep learning, ensemble models, and reinforcement learning. Examples will be drawn form the entire spectrum of energy applications to illustrate the applications of ML approaches. The hands-on use of Python notebooks will be a key aspect of the course.