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

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High-Throughput and Autonomous Grazing Incidence X-ray Diffraction Mapping of Organic Combinatorial Thin-Film Library Driven by Machine Learning.
ACS Combinatorial Science Pub Date : 2020-06-17 , DOI: 10.1021/acscombsci.0c00037
Shingo Maruyama ₁ , Kana Ouchi ₁ , Tomoyuki Koganezawa ₂ , Yuji Matsumoto ₁

Affiliation

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.

中文翻译：

机器学习驱动的有机组合薄膜库的高通量和自主掠入射X射线衍射图。

高通量X射线衍射（XRD）是加速材料研究的必不可少的技术之一。然而，在表征有机材料薄膜库的情况下，具有大束斑尺寸的常规XRD分析可能不是最合适的，在这种情况下，将在不同工艺条件下制备的各种薄膜集成在单个基板上。在这里，我们证明高分辨率掠入射XRD映射分析可用于此目的：使用同步加速器X射线X射线分析了组成和生长温度沿两个正交轴变化的二维有机组合薄膜库。。此外，

更新日期：2020-07-13

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