| Abstract Scope |
Autonomous research systems continually learn by adaptively planning and executing campaigns of physical and/or in silico experiments to achieve a scientific or engineering goal without direct human intervention. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design. General autonomous science systems face several challenges: learning to reliably synthesize materials, mapping material specification and processing to structure and properties, incorporating offline data streams, and incorporating prior theoretical and data-driven knowledge. As the materials community surmounts these challenges, closed-loop automated materials synthesis and characterization platforms offer much more than a means of engineering materials properties and performance through black-box optimization algorithms: they offer the potential to develop and deploy new algorithms for generating and testing scientific hypotheses.
I will present two exemplar autonomous systems for alloy design that are being developed at NIST, focusing on technical and methodological aspects of building and deploying robust closed-loop synthesis and characterization platforms. The first is an autonomous X-ray diffraction system that performs active cluster analysis to efficiently map composition-temperature phase diagrams using composition spread thin films. The second is an autonomous scanning droplet cell (ASDC) designed for on-demand alloy electrodeposition and real-time electrochemical characterization for investigating the corrosion-resistance properties of multicomponent alloys. Our initial studies focus on systems that are likely to form corrosion-resistant metallic glasses (MGs) and single-phase multi-principle element alloys (MPEAs).. |