MAE Department - PG Seminar - AI-driven combinatorial approach to materials and autonomous experimentation
We have been working on a variety of applications of machine learning for materials exploration. As a branch of machine learning, active learning has attracted much attention recently, as it can effectively help navigate experimental sequences in materials research in real time. We are incorporating active learning in combinatorial exploration of functional materials in autonomous modes. Autonomous experimentation can be used to reduce the number of required experimental cycles by an order of magnitude or more. The array format with which samples of different compositions are laid out on combinatorial libraries is particularly conducive to active learning. We have recently demonstrated autonomous control of unit cell-level growth of functional thin films implemented in pulsed laser deposition. Dynamic analysis of reflection high-energy electron diffraction images is used to autonomously navigate multi-dimensional deposition parameter space in order to rapidly identify the optimum set of growth parameters for fabricating the targeted materials phase. I will also discuss other autonomous experimentation projects we are carrying out.
Ichiro Takeuchi is a professor of materials science and engineering and affiliate professor of physics at the University of Maryland. He received his Ph.D. in physics from the University of Maryland in 1996. Prior to joining the University of Maryland faculty, he was a postdoctoral research associate at Lawrence Berkeley National Laboratory, where he helped pioneer the combinatorial materials synthesis strategy. Takeuchi’s research program is focused on combinatorial exploration of novel functional materials, development of elastocaloric materials and systems, and superconducting devices. Since 2009, Takeuchi has also served as the CTO of Maryland Energy & Sensor Technologies, LLC, a start-up dedicated to development of elastocaloric cooling systems. Takeuchi is a fellow of the American Physical Society and the Japan Society of Applied Physics.