The course is a part of the Atomistic Materials Informatics: From First Principles to Machine Learning Methods course, approved by the Deep Tech Talent Initiative.
This four-lecture course explores the exciting intersection of machine learning (ML) and atomistic materials engineering, a powerful approach to materials modeling alleviating the limitations of traditional methods, such as DFT. We will look into the theoretical foundations of ML techniques and their applications using cutting-edge tools and frameworks to analyze atomistic data, construct linear atomistic models, and develop machine learning interatomic potential (MLIP) models.
Registration keyword: KAU2024