Phase Mapping in EBSD Using Convolutional Neural Networks.

The emergence of commercial electron backscatter diffraction (EBSD) equipment ushered in an era of information rich maps produced by determining the orientation of user-selected crystal structures. Since then, a technological revolution has occurred in the quality, rate detection, and analysis of these diffractions patterns. The next revolution in EBSD is the ability to directly utilize the information rich diffraction patterns in a high-throughput manner. Aided by machine learning techniques, this new methodology is, as demonstrated herein, capable of accurately separating phases in a material by crystal symmetry, chemistry, and even lattice parameters with fewer human decisions. This work is the first demonstration of such capabilities and addresses many of the major challenges faced in modern EBSD. Diffraction patterns are collected from a variety of samples, and a convolutional neural network, a type of machine learning algorithm, is trained to autonomously recognize the subtle differences in the diffraction patterns and output phase maps of the material. This study offers a path to machine learning coupled phase mapping as databases of EBSD patterns encompass an increasing number of the possible space groups, chemistry changes, and lattice parameter variations.

Crystal symmetry determination in electron diffraction using machine learning.

Electron backscatter diffraction (EBSD) is one of the primary tools for crystal structure determination. However, this method requires human input to select potential phases for Hough-based or dictionary pattern matching and is not well suited for phase identification. Automated phase identification is the first step in making EBSD into a high-throughput technique. We used a machine learning-based approach and developed a general methodology for rapid and autonomous identification of the crystal symmetry from EBSD patterns. We evaluated our algorithm with diffraction patterns from materials outside the training set. The neural network assigned importance to the same symmetry features that a crystallographer would use for structure identification.

Effects of repetitive transcranial magnetic stimulation (rTMS) on attribution of movement to ambiguous stimuli and EEG mu suppression.

Recent research suggests that attributing human movement to ambiguous and static Rorschach stimuli (M responses) is associated with EEG mu suppression, and that disrupting the left inferior gyrus (LIFG; a putative area implicated in mirroring activity) decreases the tendency to see human movement when exposed to the Rorschach ambiguous stimuli. The current study aimed to test whether disrupting the LIFG via repetitive transcranial stimulation (rTMS) would decrease both the number of human movement attributions and EEG mu suppression. Each participant was exposed to the Rorschach stimuli twice, i.e., during a baseline condition (without rTMS but with EEG recording) and soon after rTMS (TMS condition with EEG recording). Experimental group (N = 15) was stimulated over the LIFG, while the control group (N = 13) was stimulated over the Vertex. As expected, disrupting the LIFG but not Vertex, decreased the number of M attributions provided by the participants exposed to the Rorschach stimuli, with a significant interaction effect. Unexpectedly, however, rTMS did not significantly influence EEG mu suppression.