LiNiO2/NiO Phase Prediction Using Artificial Neural Networks
Abstract
In the realm of material analysis, identifying different material phases is of key importance. Artificial intelligence in the form of neural networks provides a very fast and, once trained, computationally inexpensive method for analysing large amounts of image data, like the sets of diffraction patterns generated during four-dimensional scanning transmission electron microscopy (4DSTEM) dataset acquisition. In this work, we train multiple network architectures on images of this type to distinguish between the phases of LiNiO2 and NiO, an important and challenging distinction in the lithium-ion battery community, since the formation of NiO limits battery capacity. We test both classical convolutional neural networks (CNNs) and different forms of vision transformers (ViTs). Our networks are trained on synthetic images and tested on experimentally recorded diffraction patterns. Additionally, we also investigate the decision-making of our networks using the GradCAM method. We test our networks on both synthetic as well as experimental diffraction patterns. Our networks exhibit very robust results, especially when dealing with highly varying data, an area where traditional template-matching methods typically struggle.
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BibTeX
@misc{scheunert2026linio2nio,
title = {{LiNiO2/NiO Phase Prediction Using Artificial Neural Networks}},
author = {Jonas Scheunert and Shamail Ahmed and Thomas Demuth and Andreas Beyer and Kerstin Volz},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.22604},
eprint = {2609.22604},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.22604},
}
RIS
TY - UNPB
TI - LiNiO2/NiO Phase Prediction Using Artificial Neural Networks
AU - Jonas Scheunert
AU - Shamail Ahmed
AU - Thomas Demuth
AU - Andreas Beyer
AU - Kerstin Volz
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.22604
UR - https://arxiv.org/abs/2609.22604
AB - In the realm of material analysis, identifying different material phases is of key importance. Artificial intelligence in the form of neural networks provides a very fast and, once trained, computationally inexpensive method for analysing large amounts of image data, like the sets of diffraction patterns generated during four-dimensional scanning transmission electron microscopy (4DSTEM) dataset acquisition. In this work, we train multiple network architectures on images of this type to distinguish between the phases of LiNiO2 and NiO, an important and challenging distinction in the lithium-ion battery community, since the formation of NiO limits battery capacity. We test both classical convolutional neural networks (CNNs) and different forms of vision transformers (ViTs). Our networks are trained on synthetic images and tested on experimentally recorded diffraction patterns. Additionally, we also investigate the decision-making of our networks using the GradCAM method. We test our networks on both synthetic as well as experimental diffraction patterns. Our networks exhibit very robust results, especially when dealing with highly varying data, an area where traditional template-matching methods typically struggle.
ER -