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

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.

A simple array integrating machine learning for identification of flavonoids in red wines

“…The fluorescence intensity changes of three sensor elements towards 14 flavonoids were obtained and subjected to different machine learning techniques to obtain a suitable analysis method, 36–42 and the results are summarized in Fig. 4B.…”

“…Meanwhile, the various sites of the boronic acid substitutions can lead to differences in the binding and assembly of avonoids. The uorescence intensity changes of three sensor elements towards 14 avonoids were obtained and subjected to different machine learning techniques to obtain a suitable analysis method, [36][37][38][39][40][41][42] and the results are summarized in Fig. 4B.…”

Compositionally graded crystals as a revived approach for new crystal engineering for the exploration of novel functionalities

“…For this reason, we would like to emphasize that this is arguably one of the biggest challenges that must be overcome to further develop compositionally graded crystal materials with new functionalities. Recent progress in machine learning and/or AI technologies boosted by large-scale computation has a great impact on the field of modern materials science [55][56][57][58][59] and should be helpful. For example, computational calculations using large material systems, such as NCGCs, will become more accessible within a realistic time scale.…”

Epitaxial growth of copper phthalocyanine on a large single-grain of thin film alkyl perylene diimide

“…The substrate temperature was controlled by a ceramic heater attached to a stainless-steel substrate stage. To effectively investigate the growth temperature dependence, a temperature gradient technique 27) was appropriately used for fabricating samples.…”