Real-Time Autonomous Combinatorial Experimentation: From Atomic Layer Synthesis to Metal Additive Manufacturing

We are incorporating active learning in combinatorial exploration of functional materials in autonomous modes. Self-navigating 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 including metal additive manufacturing. This work is funded by NIST, ONR, and SRC.