Coherent X-ray diffraction imaging (CDI) is a powerful technique for <i>operando</i> characterization. Visualizing defects, dynamics, and structural evolution using CDI<i>,</i> however, remains a grand challenge since state-of-the-art iterative reconstruction algorithms for CDI data are time-consuming and computationally expensive, which precludes real-time feedback. Furthermore, the reconstruction algorithms require human inputs to guide their convergence, which is a very subjective process. I will describe our work in the use of deep convolutional networks (CDI NN) in accelerating the analysis of, and potentially increasing the robustness of image recovery from 3D X-ray diffraction data. Once trained, CDI NN is hundreds of times faster than traditional phase retrieval algorithms used for image reconstruction from coherent diffraction data, opening up the prospect of real-time 3D imaging at the nanoscale. Our networks are designed to be ‘physics-aware’ in multiple aspects; in that the physics of the transform is explicitly enforced in the training of the network, and the training data is drawn from a distribution that is representative of the physics of the material. We further refine the NN’s prediction through automatic differentiation of the forward model that enables maximum accuracy at lowest computational cost. While we have used the phase retrieval problem in 3D as an example, our integrated machine learning and automatic differentiation approach is widely across inverse problems.

Real-time 3D nanoscale imaging enabled by deep learning and automatic differentiation

Macromolecular Chemistry: The Second Century

[Author Benefits](https://scimeetings.acs.org/?utm_source=pubs_content_marketing&utm_medium=website&utm_campaign=0320_MCF_NPI_Launch_Spring_Homepage&ref=pubs_content_marketing)

[How to use SciMeetings](https://storage.googleapis.com/dl.morressier.com/acs/SciMeetings-ACSSpring2020-HowToGuide_M3020-Final.pdf)

[Frequently Asked Questions](https://storage.googleapis.com/dl.morressier.com/acs/SciMtng_ACSNMSp2020_FAQ_M3120_Final.pdf)

ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.