  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.
