| AVS 63rd International Symposium & Exhibition | |
| Applied Surface Science | Tuesday Sessions |
| Session AS+SS-TuA |
| Session: | Data Analytics in Surface Science and Nanoscience |
| Presenter: | Stephen Jesse, Oak Ridge National Laboratory |
| Authors: | E.J. Lingerfelt, Oak Ridge National Laboratory A. Belianinov, Oak Ridge National Laboratory E. Endeve, Oak Ridge National Laboratory O.S. Ovchinnikov, Vanderbilt University S. Somnath, Oak Ridge National Laboratory R.K. Archiblad, Oak Ridge National Laboratory S.V. Kalinin, Oak Ridge National Laboratory S. Jesse, Oak Ridge National Laboratory |
| Correspondent: | Click to Email |
Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now—with the rise of multimodal acquisition systems and the associated processing capability—the era of multidimensional, informationally dense data sets has arrived. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to perform scalable data analysis and simulation via an intuitive, cross-platform client user interface. This framework delivers authenticated, “push-button” execution of complex user workflows that deploy data analysis algorithms and computational simulations in HPC environments like Titan at the Oak Ridge Leadership Computing Facility (OLCF).
Here, we address the underlying HPC needs for characterization in the material science community, elaborate how BEAM’s design and infrastructure tackle those needs, and present a small sub-set of user cases where scientists utilized BEAM across a broad range of analytical techniques and analysis modes. BEAM system will be demonstrated for 4D Ronchigram analysis and property extraction of atomically resolved STEM (Scanning Transmission Electron Microscopy) data, parallel spectroscopic curve fitting in SPM (Scanning Probe Microscopy) data, and image segmentation.
Acknowledgements
This work is partially supported by the Laboratory Directed Research and Development (LDRD) program at ORNL, which is managed by UT-Battelle, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC05-00OR22725 (E.J.L., A.B., E.E., O.O., S.S., C.T.S., S.V.K., M.S., and S.J.). This research was conducted at the Center for Nanophase Materials Sciences and the Spallation Neutron Source, which are DOE Office of Science User Facilities. Research by J.M.B. is supported by the Center for Accelerating Materials Modeling (CAMM), which is funded by DOE Basic Energy Sciences under FWP-3ERKCSNL. This research used resources of ORNL's Compute and Data Environment for Science (CADES) and the Oak Ridge Leadership Computing Facility (OLCF), which are supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC05-00OR22725. The mathematical aspects were sponsored by the applied mathematics program at the DOE by the ACUMEN project.