| Abstract Scope |
Potential materials design spaces are broader than ever. Simultaneously, application requirements are becoming more stringent. As design complexity grows, a seamless integration between experiments, computation, and AI becomes critical. However, experimental testing poses a bottleneck. Conventional, manual testing is time-consuming, expensive, difficult to scale, and associated with long feedback delays. High-throughput, automated experiments can overcome this barrier to efficiently navigate the complex thermodynamics and kinetics of phase formation. To this end, at UW-Madison we are building an autonomous, self-driving laboratory platform called “AlloyBot”. Robotics, automation, and AI are integrated to autonomously synthesize, characterize, and test over 100 new materials per week. While AlloyBot has been developed with an initial focus on structural materials, there are various opportunities to transfer concepts and tools to functional applications including electronic materials. |