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.
Keywords: Scanning probe microscopy; Multivariate statistical analysis; High-performance computing