The summer school “Machine Learning for Materials Hard and Soft”, which took place between July 11th and 22nd, was jointly organized by the Erwin Schrödinger Institute (ESI) the Doctoral College Advanced Functional Materials (DCAFM) and the Special Research Area TACO. According to one of the co-organizers, TACO’s Christoph Dellago (P12), it “was a great success. Following a mathematical introduction and a lecture about image analysis, the participants learned about ML for electronic structure, machine learned force fields, statistical sampling and materials design in lectures and hands-on tutorials. A particular highlight was the day with research seminars and short talks from industry. During the two weeks of the school, the participants worked hard, but I think that they had a great experience and went home with a lot of new knowledge and inspiration for their own research.“
The TACO students were quite exited. Experimentalists Alex Imre (P02) reports:
“For me the summer school was a very good experience, where I think I learned a lot and met very interesting people. Even though I’m just a lowly experimentalist, I was able to follow most of the material, which I take as a big win 🙂
In all seriousness, though, I really appreciated the chance to see what the state of the art in machine learning is in our field. I was a bit skeptical before I started, but I have to say I’m highly impressed with the progress people have made and continue to make in this area. I hope I can implement some of the things I learned in my own work in ViPErLEED sooner rather than later.“
Also the theoreticians were impressed – Ralf Wanzenböck (P09) stated:
“The topics and speakers were very well chosen. I especially enjoyed that all speakers were approachable for discussion and questions.“
