Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets

Alex Belianinov, Rama Vasudevan, Evgheni Strelcov, Chad Steed, Sang Mo Yang, Alexander Tselev, Stephen Jesse, Michael Biegalski, Galen Shipman, Christopher Symons, Albina Borisevich, Rick Archibald, Sergei Kalinin

Research output: Contribution to journalReview articlepeer-review

96 Scopus citations

Abstract

The development of electron and scanning probe microscopies in the second half of the twentieth century has produced spectacular images of the internal structure and composition of matter with nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition, and analysis. Advances in imaging technology in the beginning of the twenty-first century have opened the proverbial floodgates on the availability of high-veracity information on structure and functionality. From the hardware perspective, high-resolution imaging methods now routinely resolve atomic positions with approximately picometer precision, allowing for quantitative measurements of individual bond lengths and angles. Similarly, functional imaging often leads to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this multidimensional structural and functional data into physically and chemically relevant information.

Original languageEnglish
Article number6
JournalAdvanced Structural and Chemical Imaging
Volume1
Issue number1
DOIs
StatePublished - Dec 1 2015

Funding

This research was sponsored by the Division of Materials Sciences and Engineering, BES, DOE (RKV, AT, SVK). The data analysis portion of this research (ES, MB) was conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. Research related to atomic resolution imaging (AB, AB, SJ) was sponsored by Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy. The authors gratefully acknowledge Dr. S. Zhang (Penn. State) for providing the PMN-PT ferroelectric relaxor sample as well as Dr. Ying-Hao Chu and Ying-Hui Hsieh for providing BFO-CFO nanocomposite samples. SMY acknowledges the support by IBS-R009-D1, Korea.

Keywords

  • High-performance computing
  • Multivariate statistical analysis
  • Scanning probe microscopy

Fingerprint

Dive into the research topics of 'Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets'. Together they form a unique fingerprint.

Cite this