AI-Driven Phase Identification from X-ray Hyperspectral Imaging of cycled Na-ion Cathode Materials

2026-03-08

AI Summary

This study addresses the challenge of spatially heterogeneous phase transformations in sodium-ion battery cathodes, which arise from limited Na⁺ diffusion during cycling and lead to multiphase coexistence and locally non-uniform reactions that impede mechanistic understanding and performance optimization. To resolve this, the authors propose a novel approach combining Gaussian mixture variational autoencoders (GMVAE) with Pearson correlation analysis to interpret sparsely sampled scanning transmission X-ray microscopy (STXM) hyperspectral data. This method enables nanoscale mapping of multiphase distributions within a micrometer-scale field of view, effectively identifying grain boundaries, diffuse regions, and intermediate phases with enhanced reliability. Applied to individual NaxV₂(PO₄)₂F₃ particles, the technique successfully uncovers the spatial distribution of sodium content and nanoscale phase evolution across different states of charge.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Matrix & Tensor Methods
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Na-ion batteries have emerged as viable candidates for large-scale energy storage applica- tions due to resource abundance and cost advantages. The constraints imposed on their performance and durability, for instance, by complex phase transformations in positive electrode materials during electrochemical cycling, can be addressed and are thus not detrimental to their development. However, diffusion-limited Na-ion transport can drive spatially heterogeneous phase nucleation and propagation, leading to multiphase coexis- tence and locally non-uniform electrochemical activity, generating complex reaction path- ways that challenge both mechanistic understanding and predictive material optimization. These challenges can be addressed by investigating single-crystalline regions of materials, i.e. down to the scale of individual particles, although such analyses are often constrained by energetically and/or spatially sparse hyperspectral datasets. Here, we developed an AI-driven method to process hyperspectral data under sparse sampling conditions and generate multiphase maps with nanometer-scale resolution over a micrometer-scale field of view. We applied this processing on scanning transmission X-ray microscopy (STXM) data to determine the distribution and coexistence of phases in individual particles of NaxV2(PO4)2F3 cathode materials, at different states of charge. The methodology relies on a workflow which combines a Gaussian mixture variational autoencoder (GMVAE) algorithm with the Pearson corre- lation coefficient to identify the sodium content and map their spatial distribution. Our approach reveals nanoscale phase heterogeneity and evolution within individual particles, and improves the reliability of phase detection by identifying ambiguity zones, false assign- ments, and transition phases localized at grain boundaries.

Problem

• phase heterogeneity
• Na-ion batteries
• hyperspectral imaging
• phase transformation
• electrochemical cycling

Innovation

• AI-driven phase identification
• hyperspectral imaging
• Gaussian mixture variational autoencoder
• nanoscale phase mapping
• Na-ion cathode materials