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Growth of high quality two-dimensional transition metal dichalcogenide monolayers with the desired microstructure and morphology is critical for enabling key technological solutions. This is a non-trivial problem because the processing space is vast and lack of a priori guidelines impedes rapid progress. A machine learning approach is discussed that leverages the data present in published growth experiments to predict growth performance in regions of unexplored parameter space. Starting from the literature data on MoS2 thin films grown using chemical vapor deposition (CVD), a database is manually constructed. Unsupervised and supervised machine learning methods are used to learn from the compiled data by extracting trends that underlie the formation of MoS2 monolayers. Design rules are uncovered that establish the phase boundaries classifying monolayers from other possible outcomes, which offers future guidance of CVD experiments.

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