アルゴンヌの研究者は、X線データの隙間を埋める自動的な技術を開発しました。 Argonne researchers have created an automatic technique that can fill in gaps in X-ray data.
2022-11-03 アルゴンヌ国立研究所(ANL)
AutoPhaseNNと呼ばれるこの技術は、機械学習と呼ばれる手法に基づいており、特定の実験データでアルゴリズムを学習させ、それを使って現在の実験で最も可能性の高い結果を選択する。
AutoPhaseNNは、「教師なし」機械学習の一例である。つまり、コンピューターアルゴリズムは、より正確で効率的な計算方法を自らの経験から学習するのであって、すでに解明されているラベル付き解法で学習する必要はない。
＜関連情報＞
- https://www.anl.gov/article/scientists-develop-new-algorithm-that-may-provide-insights-into-battery-corrosion
- https://www.nature.com/articles/s41524-022-00803-w
AutoPhaseNN：3次元ナノスケールブラッグコヒーレント回折イメージングの教師なし物理認識型深層学習 AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging
Yudong Yao,Henry Chan,Subramanian Sankaranarayanan,Prasanna Balaprakash,Ross J. Harder & Mathew J. Cherukara
NPJ Computational Materials Published:03 June 2022
DOI:https://doi.org/10.1038/s41524-022-00803-w
Abstract
The problem of phase retrieval underlies various imaging methods from astronomy to nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore computationally expensive. Deep learning (DL) models have been developed to either provide learned priors or completely replace phase retrieval. However, such models require vast amounts of labeled data, which can only be obtained through simulation or performing computationally prohibitive phase retrieval on experimental datasets. Using 3D X-ray Bragg coherent diffraction imaging (BCDI) as a representative technique, we demonstrate AutoPhaseNN, a DL-based approach which learns to solve the phase problem without labeled data. By incorporating the imaging physics into the DL model during training, AutoPhaseNN learns to invert 3D BCDI data in a single shot without ever being shown real space images. Once trained, AutoPhaseNN can be effectively used in the 3D BCDI data inversion about 100× faster than iterative phase retrieval methods while providing comparable image quality.
