Machine learning technique speeds up crystal structure determination
Nanoengineers at the University of California San Diego have developed a computer-based method that could make it less labor-intensive to determine the crystal structures of various materials and molecules, including alloys, proteins and pharmaceuticals. The method uses a machine learning algorithm, similar to the type used in facial recognition and self-driving cars, to independently analyze electron diffraction patterns, and do so with at least 95% accuracy.
The work is published in the Jan. 31 issue of Science.
A team led by UC San Diego nanoengineering professor Kenneth Vecchio and his Ph.D. student Kevin Kaufmann, who is the first author of the paper, developed the new approach. Their method involves using a scanning electron microscope (SEM) to collect electron backscatter diffraction (EBSD) patterns. Compared to other electron diffraction techniques, such as those in transmission electron microscopy (TEM), SEM-based EBSD can be performed on large samples and analyzed at multiple length scales. This provides local sub-micron information mapped to centimeter scales. For example, a modern EBSD system enables determination of fine-scale grain structures, crystal orientations, relative residual stress or strain, and other information in a single scan of the sample.
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