ML4MatSci School
… is designed to equip current and prospective PhD students with the essential knowledge and practical skills needed to apply machine learning techniques in the field of Materials Informatics.
Through a combination of lectures, hands-on sessions, and collaborative discussions, participants will gain a solid foundation for integrating data-driven approaches into their research.
Key Scientific Focus Areas
- Fundamentals of Machine Learning & Artificial Intelligence
Core concepts, workflows, and practical considerations - Image Processing for Materials Characterization
From segmentation to feature extraction in microscopy and beyond - Large Language Models (LLMs) & Their Emerging Applications
Using LLMs for scientific text mining, synthesis, and discovery - Datasets, Data Quality & Evaluation Metrics
How to prepare, validate, and measure the impact of your data - Bayesian Optimization & Active Learning
Smart experimentation and efficient exploration of design spaces - Advanced Topics in AI-Driven Materials Science
Including:- Generative models (e.g., for molecule or structure generation)
- Explainable AI (XAI)
- Multi-modal learning
- AI for materials discovery pipelines
Agenda – Registration
More information on the ML4MatSci School like agenda and registration you find here.
