AutoBot found that high-quality films can be synthesized at relative humidity levels between 5 and 25% by carefully tuning the other three synthesis parameters.

“This humidity range does not require stringent environmental controls,” said Ansuman Halder, a Berkeley Lab postdoctoral researcher and co-first author of the research paper. “The finding lays important groundwork for the development of commercial manufacturing facilities.”

Another insight was that humidity levels above 25% destabilized the material during the deposition process, resulting in poor film quality. The team explained and validated this finding by manually performing photoluminescence spectroscopy during film synthesis.

AutoBot’s performance was impressive. By identifying the most informative experiments, the algorithms rapidly learned how the synthesis parameters influence film quality.

“This strong performance was demonstrated by a dramatic decline in the algorithms’ learning rate after AutoBot sampled less than 1% of the 5000-plus parameter combinations,” said Maher Alghalayini, a Berkeley Lab postdoctoral scholar and co-first author. “Because new experiments were not changing the algorithms’ material quality predictions at this point, we decided to stop performing experiments.”

An innovative aspect of the study was “multimodal data fusion.” This involved using various data science and mathematical tools to integrate the disparate datasets and images from the three characterization techniques into a single metric for material quality. The idea was to quantify the results so that they were usable by the machine learning algorithms. For example, collaborators at the University of Washington designed an approach to convert the photoluminescence images into a single number based on how the light intensity varied across the images.

This research was supported by the Department of Energy’s Office of Science and was part of the Office of Science’s Early Career Research Program.

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