DONUT用物理模型加速纳米衍射实时分析，无需标注数据
DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysis
- 将可微分几何衍射模型嵌入神经网络架构
- 实测效率比传统方法快200倍以上，准确提取晶格应变与取向
- 无需标注数据即可训练，适合实验科学实时分析场景
相干X射线散射技术对纳米尺度材料结构研究至关重要。尽管技术进步使实验更易获取，但实时分析仍受伪影和计算负担制约。在扫描纳米衍射显微术中，发散光束与样品局部结构的卷积进一步加剧挑战。为此，我们提出DONUT（Diffraction with Optics for Nanobeam by Unsupervised Training），一种物理感知的无监督神经网络，用于快速自动分析纳米束衍射数据。通过在架构中直接嵌入可微分几何衍射模型，DONUT 实现了晶格应变与取向的实时预测。关键在于，该方法不依赖标注数据或预训练，克服了监督学习在X射线科学中的根本限制。实验表明，DONUT在200倍以上速度下准确提取数据中所有特征。
原文摘要 · Abstract (English)
Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample's local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.
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