新たなピークを迎える。人工知能でX線データ解析を強化(Hitting a new peak: Scientists enhance X-ray data analysis with artificial intelligence) | テック・アイ技術情報研究所

新たなピークを迎える。人工知能でX線データ解析を強化(Hitting a new peak: Scientists enhance X-ray data analysis with artificial intelligence)

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X線結晶学の新しい技術により、研究者はリアルタイムで実験を調整できる。 New technique for X-ray crystallography allows researchers to adjust their experiments in real time.

2022-05-11 アルゴンヌ国立研究所(ANL)

米国エネルギー省(DOE)アルゴンヌ国立研究所の研究者グループは、高エネルギーX線実験のデータを分析するという困難な作業を行うために、AIを活用しています。BraggNNと呼ばれるニューラルネットワークベースの新しい手法により、アルゴンヌのチームはブラッグピーク(微小な個々の結晶の位置と向きを示すデータポイント)をより正確に、従来の数分の一の時間で特定できるようになりました。

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BraggNN: ディープラーニングを用いた高速X線ブラッグピーク解析 BraggNN: fast X-ray Bragg peak analysis using deep learning

Zhengchun Liu, Hemant Sharma, Jun-Sang Park, Peter Kenesei, Antonino Miceli, Jonathan Almer, Rajkumar Kettimuthu and Ian Foster
International Union of Crystallography  Published:10 December 2021
DOI:https://doi.org/10.1107/S2052252521011258

新たなピークを迎える。人工知能でX線データ解析を強化(Hitting a new peak: Scientists enhance X-ray data analysis with artificial intelligence)

Abstract

X-ray diffraction based microscopy techniques such as high-energy diffraction microscopy (HEDM) rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak-shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback in experiments. To this end, we propose BraggNN, a deep-learning based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, peak center-of-mass positions obtained from BraggNN deviate less than 0.29 and 0.57 pixels for 75 and 95% of the peaks, respectively, from positions obtained using conventional pseudo-Voigt fitting (Euclidean distance). When applied to a real experimental dataset and using grain positions from near-field HEDM reconstruction as ground-truth, grain positions using BraggNN result in 15% smaller errors compared with those calculated using pseudo-Voigt. Recent advances in deep-learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster on a consumer-class GPU card with out-of-the-box software.

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