Deep learning-enabled large-scale analysis of particle geometry-lithiation correlations in battery cathode materials

TL;DR

This paper uses deep learning to analyze nanoparticle images, revealing how particle shape influences lithiation patterns in battery cathode materials, which can guide design improvements.

Contribution

It introduces a novel deep learning-based segmentation and correlation framework for linking particle geometry with lithiation phases in battery materials.

Findings

Particle size and shape significantly affect lithiation heterogeneity.

Optimized particle geometries can improve lithiation uniformity.

The method enables detailed phase mapping of nanoparticles.

Abstract

A deep learning model is employed to address the challenging problem of V2O5 nanoparticle segmentation and the correlation between the chemical composition and the geometrical features of lithiated V2O5 nanoparticles as an exemplar of a phase-transforming battery cathode material. First, the deep learning-enabled segmentation model is integrated with the singular value decomposition technique and a spectral database to generate accurate composition and phase maps capturing lithiation heterogeneities as imaged using scanning transmission X-ray microscopy. These phase maps act as the output properties for correlation analysis. Subsequently, the quantitative influences of the geometrical features of nanoparticles such as the particle size (i.e., projected perimeter and area), the aspect ratio, circularity, convexity, and orientation on the lithiation phase maps are revealed. These findings inform strategies to improve lithiation uniformity and reduce stress in phase-transforming lithium battery materials via optimized particle geometry.