An unsupervised machine learning technique was developed for visualizing and quantifying micro-structures in three-dimensional (3D) samples with data from molecular dynamics simulations, particle tracking data, and experiments.
Significance and Impact
The technique provides a way to quickly identify, track, and quantify complex microstructural features that impact the observed behavior of materials with many technological applications.
- The technique provides microstructural information regarding e.g. grains and their size distributions in 3D polycrystalline samples, voids and porosity in 3D polymeric samples, and micellar size distribution in 3D complex fluids.
- Center for Nanoscale Materials (CNM) Carbon HPC, Argonne Leadership Computing Facility, National Energy Scientific Computing Center and Argonne’s Laboratory Computing Resource Center were used.
Work was performed in part at CNM.
About Argonne’s Center for Nanoscale Materials
The Center for Nanoscale Materials is one of the five DOE Nanoscale Science Research Centers, premier national user facilities for interdisciplinary research at the nanoscale supported by the DOE Office of Science. Together the NSRCs comprise a suite of complementary facilities that provide researchers with state-of-the-art capabilities to fabricate, process, characterize and model nanoscale materials, and constitute the largest infrastructure investment of the National Nanotechnology Initiative. The NSRCs are located at DOE’s Argonne, Brookhaven, Lawrence Berkeley, Oak Ridge, Sandia and Los Alamos National Laboratories. For more information about the DOE NSRCs, please visit https://science.osti.gov/User-Facilities/User-Facilities-at-a-Glance.
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