Join us at the Lawrence Berkeley National Lab, CA, from August 13-15 for activities and workshops for applying machine learning to material science
Robotics and AI are regarded as transformative tools for scientific discovery and integral components of future laboratories. The primary goal of the summer school is to engage students in laboratory automation for the discovery of energy materials. Through lectures, coding tutorials, and robotic synthesis experiments, participants will explore closed-loop experiment design, machine learning, data handling and visualization, as well as hands-on lab work to create thin-film semiconductors using a robotic platform.
We aim to foster the development of the future workforce in materials science, engineering, computer science, physics, and related fields, anticipating that science and research will increasingly integrate artificial intelligence and machine learning (AI/ML).
The summer school is a 2.5 day event and open to all students and postdocs from interdisciplinary backgrounds. No prior coding experience is required. Participation in hands-on lab experiments is limited to selected participants due to space constraints.
Schedule
Get hands-on experience with robotic synthesis!
Learn scientific computing for machine learning!
Apply active learning algorithms for materials optimization!
Who is eligible to register for the summer course?
- Those registered for the 2025 Molecular Foundary Annual Meeting.
- Professionals, Postdocs, Graduate Students, Undergraduate Students.
- Little to no experience in Python.
Readings from our Hosts
Learn more thourgh some of the publications from our host!