  Ever increasing hardware capabilities and computation powers have made
acquisition and analysis of big scientific data at the nanoscale routine,
though much of the data acquired often turns out to be redundant, noisy, and/or
irrelevant to the problems of interests, and it remains nontrivial to draw
clear mechanistic insights from pure data analytics. In this work, we use
scanning probe microscopy (SPM) as an example to demonstrate deep data
methodology, transitioning from brute force analytics such as data mining,
correlation analysis, and unsupervised classification to informed and/or
targeted causative data analytics built on sound physical understanding. Three
key ingredients of such deep data analytics are presented. A sequential
excitation scanning probe microscopy (SE-SPM) technique is first adopted to
acquire high quality, efficient, and physically relevant data, which can be
easily implemented on any standard atomic force microscope (AFM). Brute force
physical analysis is then carried out using simple harmonic oscillator (SHO)
model, enabling us to derive intrinsic electromechanical coupling of interests.
Finally, principal component analysis (PCA) is carried out, which not only
speeds up the analysis by four orders of magnitude, but also allows a clear
physical interpretation of its modes in combination with SHO analysis. A rough
piezoelectric material has been probed using such strategy, enabling us to map
its intrinsic electromechanical properties at the nanoscale with high fidelity,
where conventional methods fail. The SE in combination with deep data
methodology can be easily adapted for other SPM techniques to probe a wide
range of functional phenomena at the nanoscale.
