Argonne researchers have created an automatic technique that can fill in gaps in X-ray data assembling 3D scientific jigsaw puzzles —
Argonne researchers have created an automatic technique that can fill in gaps in X-ray data.
“With the kind of diffraction we’re doing, getting the phase information is a challenge; it’s like figuring out how all the pieces fit together solely based on the colors you can see on each piece.” — Yudong Yao, Argonne National Laboratory. AutoPhaseNN is an example of an “unsupervised” machine learning, which means that the computer algorithm learns from its own experience how to do a computation more accurately and efficiently, without having to be trained with labeled solutions that have already been figured out, a process which usually involves human intervention.
Because the X-ray beams used to illuminate the sample are coherent — meaning they all share the same phase initially — whatever change to the phase occurs as a result of the diffraction can be mapped back to the sample itself, said Argonne nanoscientist and co-author Henry Chan.
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