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Thin films are ubiquitous in modern technology and highly useful in materialsdiscovery and design. For achieving optimal extrinsic properties theirmicrostructure needs to be controlled in a multi-parameter space, which usuallyrequires a too-high number of experiments to map. We propose to master thinfilm processing microstructure complexity and to reduce the cost ofmicrostructure design by joining combinatorial experimentation with generativedeep learning models to extract synthesis-composition-microstructure relations.A generative machine learning approach comprising a variational autoencoder anda conditional generative adversarial network predicts structure zone diagrams.We demonstrate that generative models provide a so far unseen level of qualityof generated structure zone diagrams comprising chemical and processingcomplexity for the optimization of chemical composition and processingparameters to achieve a desired microstructure.

Mastering processing-microstructure complexity through the prediction of thin film structure zone diagrams by generative machine learning models