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.
The course takes an advanced dive into the intricacies of building Machine Learning models that augment and expand the possibilities of the first-prinicples methods of Quantum Mechanics in atomistic materials simulations. The study materials cover the topics from specifics of architectures of atomistic ML models, through the details of data acquisition and preparation, to training, validation, and application of the models. The course gives practical recipes for the steps to take in building the models and overcoming some of the challenges along the way.
Registration keyword: KAU2024