基于物理信息的神经网络从单次广角FEL衍射图重构纳米颗粒
Reconstruction of nanoscale particles from single-shot wide-angle FEL diffractions patterns with physics-informed neural networks
摘要：
单次广角衍射成像是一种广泛使用的方法，用于研究非结晶物体的结构，如纳米团簇，大蛋白质甚至病毒。它的主要优点是关于物体的三维结构的信息已经包含在单个图像中。这使得它可用于重建脆弱和不可再生的颗粒，而不需要断层扫描测量。然而，目前还没有有效的数值反演算法，能够实时确定对象的结构。另一方面，神经网络在适合这种目的的图像处理任务中表现出色。在这里，我们展示了如何使用物理信息深度神经网络从单个二维广角散射模式重建体素网格上均匀凸颗粒的完整三维物体模型。我们展示了其通用的银纳米团簇的重建能力，其中网络揭示了新的几何结构，再现实验散射数据具有非常高的精度。
英文摘要：
Single-shot wide-angle diffraction imaging is a widely used method to investigate the structure of non-crystallizing objects such as nanoclusters, large proteins or even viruses. Its main advantage is that information about the three-dimensional structure of the object is already contained in a single image. This makes it useful for the reconstruction of fragile and non-reproducible particles without the need for tomographic measurements. However, currently there is no efficient numerical inversion algorithm available that is capable of determining the object's structure in real time. Neural networks, on the other hand, excel in image processing tasks suited for such purpose. Here we show how a physics-informed deep neural network can be used to reconstruct complete three-dimensional object models of uniform, convex particles on a voxel grid from single two-dimensional wide-angle scattering patterns. We demonstrate its universal reconstruction capabilities for silver nanoclusters, where the network uncovers novel geometric structures that reproduce the experimental scattering data with very high precision.
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