| AVS 66th International Symposium & Exhibition | |
| New Challenges to Reproducible Data and Analysis Focus Topic | Monday Sessions |
| Session RA+AS+NS+SS-MoA |
| Session: | Quantitative Surface Analysis II/Big Data, Theory and Reproducibility |
| Presenter: | Mathew Cherukara, Argonne National Laboratory |
| Authors: | M. Cherukara, Argonne National Laboratory Y. Liu, Argonne National Laboratory M.V. Holt, Argonne National Laboratory H. Liu, Argonne National Laboratory T.E. Gage, Argonne National Laboratory J.G. Wen, Argonne National Laboratory I. Arslan, Argonne National Laboratory |
| Correspondent: | Click to Email |
As microscopy methods and detectors have advanced, the rates of data acquisition and the complexity of the acquired data have increased, and these are projected to increase several hundred-fold in the near future. The unique electron and X-ray imaging capabilities at the Center for Nanoscale Materials (CNM) are in a position to shed light on some of the most challenging and pressing scientific problems we face today. To fully leverage the capability of these advanced instruments, we need to design and develop effective strategies to tackle the problem of analyzing the data generated by these imaging tools, especially following facility upgrades such as the upgrade to the Advanced Photon Source (APS-U) and the commissioning of the ultrafast electron microscope (UEM).
The data problem is especially acute in the context of coherent imaging methods, ultra-fast imaging and multi-modal imaging techniques. However, analysis methods have not kept pace. It is infeasible for a human to sort through the large, complex data sets being generated from imaging experiments today. At the CNM, we apply machine learning algorithms to our suite of electron and X-ray microscopy tools. Machine learning workflows are being developed to sort through data in real-time to retain only relevant information, to invert coherently scattered data to real-space structure and strain, to automatically identify features of interest such as the presence of defects, and even to automate decision making during an imaging experiment. Such methods have the potential not only to decrease the analysis burden on the scientist, but to also increase the effectiveness of the instruments, for instance by providing real-time experimental feedback to help guide the experiment.