As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise. Here, we propose to overcome this limitation by incorporating a 3D Machine Learning (ML) model combining (optional) supervised training with unsupervised refinement. The trained ML model can rapidly provide an immediate result with high accuracy, which will benefit real-time experiments. More significantly, the Neural Network model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate loss function alone. We demonstrate significantly improved performance with experimental Bragg CDI data over traditional iterative phase retrieval algorithms.
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