Lattice Strain Evolution in Na-NMC Battery From Electron Diffraction Patterns Using Deep Learning

The potential risk of future lithium (Li) scarcity necessitates the search for an alternative chemistry such as the sodium ion batteries (SIBs) due to the abundance and low cost of sodium (Na).  The Na analogs to the Li(Ni,Mn,Co)O₂ (NMC) family of cathode materials, Na-NMC, are promising as SIB cathodes. However, cycling durability of SIB cathodes has been one of the important aspects for the performance and stability of SIBs. Lattice strain evolution during electrochemical cycling plays an important role in stability and reliability of SIB cathodes. To improve strain analysis recent advances in 4D-STEM (4D-scanning transmission electron microscope) has enabled extraction of crystallographic information from the polycrystalline samples such as the cycled Na-NMC electrode. To this end, we discuss the development of a fully automated AI/ML python-based pipeline to extract strain maps from 4D-STEM diffraction dataset. We discuss one of the targeted analysis pipelines to predict Bragg disk positions from the measured electron diffraction images of cycled NaNi_0.4Mn_0.4Co_0.2O₂ electrode. Based on the improved strain map predictions we conclude the material stability and lifetime in regard to the change in lattice parameters due to the imposed strain during the electrochemical cycling.