Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mastering processing-microstructure complexity through the prediction of
  thin film structure zone diagrams by generative machine learning models   Thin films are ubiquitous in modern technology and highly useful in materials
discovery and design. For achieving optimal extrinsic properties their
microstructure needs to be controlled in a multi-parameter space, which usually
requires a too-high number of experiments to map. We propose to master thin
film processing microstructure complexity and to reduce the cost of
microstructure design by joining combinatorial experimentation with generative
deep learning models to extract synthesis-composition-microstructure relations.
A generative machine learning approach comprising a variational autoencoder and
a conditional generative adversarial network predicts structure zone diagrams.
We demonstrate that generative models provide a so far unseen level of quality
of generated structure zone diagrams comprising chemical and processing
complexity for the optimization of chemical composition and processing
parameters to achieve a desired microstructure.

9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: eess