Hosted by: Deyu Lu

Autonomous experimentation holds the promise of revolutionizing scientific studies and accelerating materials discovery, through instruments that can explore scientific problems without human intervention. This talk will introduce basic machine-learning concepts, and describe our ongoing development of autonomous experimentation at a synchrotron x-ray scattering beamline. Deep learning (convolutional neural networks) are used to classify x-ray detector images, with performance improving when "physics awareness" is included. To close the autonomous loop, we deploy a general-purpose algorithm that selects high-value experiments to conduct, attempting to minimize both uncertainty and experimental cost. Examples from recent autonomous experiments will be presented, including measuring nanoparticle ordering, combinatorial libraries of block copolymer materials, and real-time photo-thermal processing.