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- W3089717465 abstract "Closed-loop optimization of epitaxial titanium nitride (TiN) thin-film growth was accomplished using metal-organic molecular beam epitaxy (MO-MBE) combined with a Bayesian machine-learning technique and reduced the required number of thin-film growth experiments. Epitaxial TiN thin films grown under the process conditions optimized by the Bayesian approach exhibited abrupt metal–superconductor transitions above 5 K, demonstrating a new approach to the efficient development of less-studied materials, such as transition metal nitrides. The combination of the thin-film growth technique and Bayesian approach is expected to pave the way toward accelerating the development of the automated operation of thin-film growth apparatuses." @default.
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- W3089717465 date "2021-01-01" @default.
- W3089717465 modified "2023-10-18" @default.
- W3089717465 title "Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning" @default.
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- W3089717465 doi "https://doi.org/10.1016/j.mtphys.2020.100296" @default.
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