Featured Publications & Research Highlights using the UC San Diego NE-MRC

Research Highlights

Identifying molecular structure is a crucial step in the analysis of proteins, pharmaceuticals, geology, and materials science because crystal structure often plays an important role in the properties exhibited. Electron diffraction is one of the most common methods of structure analysis in each of these fields. One of the major challenges within the world of electron diffraction is analysis of the collected diffraction patterns.

Kevin Kaufmann et al. have developed a computer algorithm that independently analyzes diffraction patterns utilizing the same type of deep learning (a subset of artificial intelligence and machine learning) algorithm as is applied in facial recognition and self-driving cars. In this work, they demonstrate the method using a scanning electron microscope (SEM) to collect electron backscatter diffraction (EBSD) patterns. 

A modern EBSD system enables determination of fine-scale grain structures, crystal orientations, relative residual stress/strain, and other information. The drawback of commercial EBSD systems is the software’s inability to determine the atomic structure of the crystalline lattices present within the material being analyzed. Using the newly designed method, the deep neural network independently analyzes each diffraction pattern to determine the lattice, out of all possible lattice structure types, with a high degree of accuracy.