  Materials synthesis platforms that are designed for autonomous
experimentation are capable of collecting multimodal diagnostic data that can
be utilized for feedback to optimize material properties. Pulsed laser
deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the
need arises to develop machine learning (ML) techniques that are capable of
extracting information from in situ diagnostics. Here, we demonstrate that
intensified-CCD image sequences of the plasma plume generated during PLD can be
used for anomaly detection and the prediction of thin film growth kinetics. We
developed a multi-output (2$+$1)D convolutional neural network regression model
that extracts deep features from plume dynamics that not only correlate with
the measured chamber pressure and incident laser energy, but more importantly,
predict parameters of an auto-catalytic film growth model derived from in situ
laser reflectivity experiments. Our results are the first demonstration of how
ML with in situ plume diagnostics data in PLD can be utilized to maintain
deposition conditions in an optimal regime. Further, 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.
