Intro to Modeling and Machine Learning

4.0 credits

This graduate elective introduces students to the foundational principles and techniques of molecular modeling and machine learning, focusing on their applications in chemical engineering. The course covers advanced molecular modeling methods, including density functional theory (DFT), molecular dynamics (MD) simulations, and grand canonical Monte Carlo (GCMC) techniques, providing a comprehensive understanding of how these tools are used to study and predict molecular behavior and properties. In parallel, the course explores machine learning (ML) approaches tailored to chemical engineering challenges, including unsupervised and supervised learning methods. Students will learn to apply regression techniques and neural network models to analyze complex datasets and enhance predictive modeling. Each topic will be introduced through lectures, and in the following weeks, students will present research techniques that utilize the introduced methods, fostering peer-to-peer learning and practical engagement. Learning Outcomes: Understand and apply key molecular modeling techniques to study molecular systems. Develop proficiency in supervised and unsupervised ML methods, including regression and neural networks. Integrate molecular modeling and ML tools to address real-world chemical engineering problems. Gain experience with computational software and frameworks used in molecular and machine learning simulations. Strengthen presentation and research skills by critically analyzing and discussing advanced techniques in the field.