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arXiv · 2409.07322

Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications

Calum Green·Sharif Ahmed·Shashidhara Marathe·Liam Perera·Alberto Leonardi·Killian Gmyrek·Daniele Dini·James Le Houx
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Abstract

Machine learning techniques are being increasingly applied in medical and physical sciences across a variety of imaging modalities; however, an important issue when developing these tools is the availability of good quality training data. Here we present a unique, multimodal synchrotron dataset of a bespoke zinc-doped Zeolite 13X sample that can be used to develop advanced deep learning and data fusion pipelines. Multi-resolution micro X-ray computed tomography was performed on a zinc-doped Zeolite 13X fragment to characterise its pores and features, before spatially resolved X-ray diffraction computed tomography was carried out to characterise the homogeneous distribution of sodium and zinc phases. Zinc absorption was controlled to create a simple, spatially isolated, two-phase material. Both raw and processed data is available as a series of Zenodo entries. Altogether we present a spatially resolved, three-dimensional, multimodal, multi-resolution dataset that can be used for the development of machine learning techniques. Such techniques include development of super-resolution, multimodal data fusion, and 3D reconstruction algorithm development.

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Calum Green, Sharif Ahmed, Shashidhara Marathe, Liam Perera, Alberto Leonardi, Killian Gmyrek, Daniele Dini, James Le Houx. 2024-09-11. Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications. https://arxiv.org/abs/2409.07322

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