LiNiO2/NiO Phase Prediction Using Artificial Neural Networks

This research applies artificial neural networks to analyze diffraction pattern datasets acquired during four-dimensional scanning transmission electron microscopy (4DSTEM). The trained networks enable fast and computationally inexpensive identification of LiNiO2 and NiO material phases from large volumes of electron microscopy image data, supporting high-throughput materials characterization and discovery.