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

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)348-355
Number of pages8
JournalACS Combinatorial Science
Volume22
Issue number7
DOIs
Publication statusPublished - 2020 Jul 13

Keywords

  • Bayesian optimization
  • GIXD
  • high-throughput mapping
  • microbeam X-ray
  • organic combinatorial thin film library

ASJC Scopus subject areas

  • Chemistry(all)

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