High-Throughput and Autonomous Grazing Incidence X-ray Diffraction Mapping of Organic Combinatorial Thin-Film Library Driven by Machine Learning

Shingo Maruyama, Kana Ouchi, Tomoyuki Koganezawa, Yuji Matsumoto

研究成果: Article査読

抄録

High-throughput X-ray diffraction (XRD) is one of the most indispensable techniques to accelerate materials research. However, the conventional XRD analysis with a large beam spot size may not best appropriate in a case for characterizing organic materials thin film libraries, in which various films prepared under different process conditions are integrated on a single substrate. Here, we demonstrate that high-resolution grazing incident XRD mapping analysis is useful for this purpose: A 2-dimensional organic combinatorial thin film library with the composition and growth temperature varied along the two orthogonal axes was successfully analyzed by using synchrotron microbeam X-ray. Moreover, we show that the time-consuming mapping process is accelerated with the aid of a machine learning technique termed as Bayesian optimization based on Gaussian process regression.

本文言語English
ページ(範囲)348-355
ページ数8
ジャーナルACS Combinatorial Science
22
7
DOI
出版ステータスPublished - 2020 7 13

ASJC Scopus subject areas

  • Chemistry(all)

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