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Several hundred plasma-assisted molecular beam epitaxy synthesis experiments of GaN and ZnO thin film crystals were organized into data sets that correlate the operating parameters selected for growth to two figures of merit: a binary determination of surface morphology, and a continuous Bragg–Williams measure of lattice ordering (S2). Quantum as well as conventional supervised machine learning algorithms were optimized and trained on the data, enabling a comparison of their generalization performance. The models displaying the best generalization performance on each data set were subsequently used to predict each figure of merit across the ZnO and GaN processing spaces. 

Quantifying disorder in physical systems can provide unique opportunities to engineer-specific properties. Here, we apply a methodology based on the approach pioneered by Bragg and Williams for metal alloys to quantify the disorder characterizing polymer fibers including polyaniline (PANI), polyaniline-polycaprolactone (PANI-PCL), and polyvinylidene difluoride (PVDF). Both PANI and PVDF possess electrical properties such as conductivity and piezoelectric response that find a wide range of applications in energy storage and tissue engineering. On the other hand, the mechanical properties of polymer fibers can be tuned by varying the concentration of PANI and PCL during synthesis. Here, we demonstrate that it is possible to control the amount of disorder characterizing a fiber, which provides a route to engineering desired values for specific material properties. The resulting measure of disorder is shown to have a direct relationship to Young’s modulus, band gap, and specific capacitance values.