STEMDL (Classification)

State of the art scanning transmission electron microscopes (STEM) produce focused electron beams with atomic dimensions and allow to capture diffraction patterns arising from the interaction of incident electrons with nanoscale material volumes.

State of the art scanning transmission electron microscopes (STEM) produce focused electron beams with atomic dimensions and allow to capture diffraction patterns arising from the interaction of incident electrons with nanoscale material volumes.

STEMDL (Classification)

State of the art scanning transmission electron microscopes (STEM) produce focused electron beams with atomic dimensions and allow to capture diffraction patterns arising from the interaction of incident electrons with nanoscale material volumes. Backing out the local atomic structure of said materials requires compute- and time-intensive analyses of these diffraction patterns (known as convergent beam electron diffraction, CBED). Traditional analyses of CBED requires iterative numerical solutions of partial differential equations and comparison with experimental data to refine the starting material configuration. This process is repeated anew for every newly acquired experimental CBED pattern and/or probed material.

In this benchmark, we used newly developed multi-GPU and multi-node electron scattering simulation codes [1] on the Summit supercomputer to generate CBED patterns from over 60,000 materials (solid-state materials), representing nearly every known crystal structure. A scaled-down version of this data [2] is used for one of the data challenges [3] at SMC 2020 conference, and the overarching goals are to: (1) explore the suitability of machine learning algorithms in the advanced analysis of CBED and (2) produce a machine learning algorithm capable of overcoming intrinsic difficulties posed by scientific datasets.

A data sample from this data set is given by a 3-d array formed by stacking various CBED patterns simulated from the same material at different distinct material projections (i.e. crystallographic orientations). Each CBED pattern is a 2-d array with float 32-bit image intensities. Associated with each data sample in the data set is a host of material attributes or properties which are, in principle, retrievable via analysis of this CBED stack. Of note are (1) 200 crystal space groups out of 230 unique mathematical discrete space groups and (2) local electron density which governs material’s property.

This benchmark consists of 2 tasks: classification for crystal space groups and reconstruction for local electron density, the example implementation of which are provided in [4] and [5].

STEMDL Specific Benchmark Targets

  1. Scientific objective(s):
    • Objective: Classification for crystal space groups
    • Formula: F1 score on validation data
    • Score: 0.9 considered converged
  2. Data
  3. Example implementation