Determining the grain orientations of battery materials from electron diffraction patterns using convolutional neural networks

Abstract Polycrystalline materials have numerous applications due to their unique properties, which are often determined by the grain orientation relationships. Hence, quantitative characterization of grain as well as interface orientation is essential to optimize these materials, particularly energy materials. Using scanning transmission electron microscopy (TEM), materials can be analysed in an extremely fine grid of scan points via electron diffraction patterns at each scan point. By matching the diffraction patterns to a simulated database, the crystal orientation of a grain at each scan point can be determined. In this work, we train convolutional neural networks on dynamically simulated diffraction patterns of LiNiO 2 , an important cathode-active material for lithium-ion batteries, to predict the orientation of grains in terms of three Euler angles for the complete fundamental orientation region. Results demonstrate that these networks outperform the conventional pattern-matching algorithm with increased accuracy and efficiency. The former can be attributed to the fact that these models are trained by data incorporating dynamical effects. This work is an attempt to apply deep learning for the analysis of TEM data to determine the grain orientation and enlighten the great potential of machine learning to accelerate the analysis of electron microscopy data, toward a high-throughput characterization technique.