Abstract Machine learning (ML) has become critical for post-acquisition data analysis in
(scanning) transmission electron microscopy,(S) TEM, imaging and spectroscopy. An …

The breadth, complexity, and volume of data generated by materials characterization using
various forms of microscopy has expanded significantly. Combined with increases in …

Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and
nanotechnology by enabling imaging and manipulation of the structure and functionality of …

Experimental science is enabled by the combination of synthesis, imaging, and functional
characterization organized into evolving discovery loop. Synthesis of new material is …

Over the past two decades, continued improvements in instrumentation hardware [1] as well
as the increased accessibility to high-performance computing (HPC) resources [2], and more …

As data sets from DOE user science facilities grow in both size and complexity there is an
urgent need for new capabilities to transfer, analyze and manage the data underlying …

Recent advances in (scanning) transmission electron microscopy have enabled a routine
generation of large volumes of high-veracity structural data on 2D and 3D materials …

In the era of big data, materials science workflows need to handle large-scale data
distribution, storage, and computation. Any of these areas can become a performance …

Multimodal chemical imaging simultaneously offers high-resolution chemical and physical
information with nanoscale and, in select cases, atomic resolution. By coupling modalities …

From emergent material descriptions to estimation of properties stemming from structures to
optimization of process parameters for achieving best performance–all key facets of …