We built a workflow for the fabrication analysis of thin films by applying machine-learning (ML) techniques directly to the measurement data. This will lower the problem in cost of synthesizing and analyzing samples t...
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We built a workflow for the fabrication analysis of thin films by applying machine-learning (ML) techniques directly to the measurement data. This will lower the problem in cost of synthesizing and analyzing samples to improve the fabrication conditions. The workflow combines two ML techniques: non-negative matrix factorization (NMF) and variational autoencoder (VAE). The measurement data were two-dimensional X-ray diffraction of indium-gallium oxide system thin films. The thin films were fabricated by physical vapor techniques under multiple conditions. First, the workflow was applied to the data of the thin films fabricated through pulsed laser deposition as a proof of concept. We found that our workflow extracted features that represented crystallinity differences in addition to substrate differences. Second, VAE was analyzed to determine whether it could generate new data from its latent space. The latent space of the VAE, which learned the extracted features, represented the relationship between the fabrication conditions such as laser intensities and crystallinity. Third, the inference ability of the new data fabricated through sputtering was evaluated. The capability of the workflow we confirmed will support researchers in improving fabrication conditions by visually comparing various fabricated samples.
The coefficient of thermal expansion (CTE) is an industrially crucial macroscopic property of polymers. Yet, there is no structure-based model expressing it with sufficient accuracy. In this work, we present two data-...
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The coefficient of thermal expansion (CTE) is an industrially crucial macroscopic property of polymers. Yet, there is no structure-based model expressing it with sufficient accuracy. In this work, we present two data-driven predictive models for the linear CTE of amorphous homopolymers in the glassy state based solely on chemical structure, showing consistent predictions. The first model is built with the SMILES-X software and is based on the simplified molecular-input line-entry system (SMILES) of polymer's repeating unit as input. The second model is built with a random forest trained on extended-connectivity fingerprints of repeating units. Both models are trained on 106 experimental data samples taken from the PoLyInfo database. The out-of-sample prediction shows a root-mean-square error of 2.65 +/- 0.09 x 10(-5) K-1 (2.58 +/- 0.09 x 10(-5) K-1), a mean absolute error of 1.71 +/- 0.06 x 10(-5) K-1 (1.61 +/- 0.06 x 10(-5) K-1) and a coefficient of determination of 0.62 +/- 0.03 (0.64 +/- 0.03) for SMILES-X (random forest). Additionally, the models are validated experimentally using a lab-prepared sample with good agreement (p-value$$ \gg $$
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