AI-Driven Phase Identification from X-ray Hyperspectral Imaging of cycled Na-ion Cathode Materials
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arXiv
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| Natura: | Preprint |
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2026
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| author | Adrar, Fayçal Folastre, Nicolas Pablos, Chloé Stanescu, Stefan Swaraj, Sufal Raghvender, Raghvender Cadiou, François Croguennec, Laurence Bugnet, Matthieu Demortière, Arnaud |
| author_facet | Adrar, Fayçal Folastre, Nicolas Pablos, Chloé Stanescu, Stefan Swaraj, Sufal Raghvender, Raghvender Cadiou, François Croguennec, Laurence Bugnet, Matthieu Demortière, Arnaud |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07666 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-Driven Phase Identification from X-ray Hyperspectral Imaging of cycled Na-ion Cathode Materials Adrar, Fayçal Folastre, Nicolas Pablos, Chloé Stanescu, Stefan Swaraj, Sufal Raghvender, Raghvender Cadiou, François Croguennec, Laurence Bugnet, Matthieu Demortière, Arnaud Materials Science Artificial Intelligence 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. |
| title | AI-Driven Phase Identification from X-ray Hyperspectral Imaging of cycled Na-ion Cathode Materials |
| topic | Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2603.07666 |