AI-driven pulsed laser deposition of nanomaterials

Recent advances in autonomous experiments that combine automated synthesis, characterization, and Artificial Intelligence (AI) enable the exploration of large parameter spaces at rates beyond what is possible by human experimentalists, promising to greatly accelerate our understanding of synthesis science. In this talk, I will discuss the infrastructure development of two highly versatile, autonomy-capable Pulsed Laser Deposition (PLD) platforms. I will then show how we incorporated in situ and real-time diagnostics and characterization, a high-throughput methodology, and cloud connectivity to enable autonomous synthesis with PLD. Next, I will show that deep learning with intensified-CCD image sequences of the plasma plume generated during PLD can be used for prediction of thin film growth kinetics. The predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD. Finally, I will discuss on-line Bayesian state estimation methods towards model-based real-time control over synthesis.