📜 Autonomous Systems for Discovery
Advanced materials hold the potential to improve our lives and our world, but traditional methods of discovery are slow and expensive. “Self-driving” laboratories (SDLs) have the power to fast-track materials discovery by using AI and robotics to run lab experiments autonomously. State-of-the-art SDLs require interdisciplinary teams and skillsets that traditional degree-based programs do not provide. To address this gap, the Acceleration Consortium @ University of Toronto presents the Autonomous Systems for Discovery certificate containing short, hands-on courses that will provide familiarity with the terminology, principles, and tools of SDLs.
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🎓 Microcourses
The Autonomous Systems for Discovery certificate consists of five core microcourses and corresponding learning outcomes:
Course Title |
Learning Outcome |
Registration Link |
Students |
|
---|---|---|---|---|
💡 |
Recreate a color-matching SDL from scratch using LEDs and a light sensor |
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📈 |
Write Python scripts to iteratively optimize materials and log results to a database |
TBD |
N/A |
|
🦾 |
Write Python scripts to control robots and orchestrate workflows |
TBD |
N/A |
|
🧑💻 |
Leverage software development tools and implement best practices |
TBD |
N/A |
|
🏢 |
Develop, defend, and execute a project proposal |
TBD |
N/A |
The microcourses progress in three stages—introduction, deeper dives, and capstone—as shown in the figure below. While the first four courses are fully remote and asychronous, the final capstone course will be conducted in-person at the AC training lab, where participants will have access to both educational and research-grade equipment.