| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Frontiers of Materials Award Symposium: Machine Learning and Autonomous Researchers for Materials Discovery and Design
|
| Presentation Title |
Autonomous Research Systems for Materials Development |
| Author(s) |
Benji Maruyama, Rahul Rao, Ahmad Islam, Jennifer Carpena , Michael Susner, Kristofer Reyes, Jay Myung, Mark Pitt |
| On-Site Speaker (Planned) |
Benji Maruyama |
| Abstract Scope |
Autonomous Research Systems like ARES™ are disrupting the research process by using AI and Machine Learning to drive closed-loop iterative research. ARES™ is our autonomous research robot capable of designing, executing and evaluating its own experiments at a rate of up to 100 iterations per day. Previously ARES taught itself to grow carbon nanotubes at controlled rates (NPJ Comp Mat 2016). Here we discuss recent research campaigns on maximizing carbon nanotube growth rates using a Bayesian optimization planner. We also use HOLMES and knowledge gradient descent to introduce advanced decision policies with local parametric models to control nanotube diameter. Implications for nanotube materials development will be discussed. Finally, we have developed a new research robot for additive manufacturing, AM ARES™, which is at the early stages of teaching itself to print structures with unknown inks. We plan to make the AM ARES™ Robot available online so that a broad community of researchers can test concepts and approaches for AI/ML and experimental design as applied to 3D printing, thus building to the larger goal of enhancing citizen science. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Advanced Materials, |