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<p>Machine learning (ML) techniques are gaining traction for materials image processing applications. In this context, Ziatdinov et al. developed AtomAI, a user-friendly and comprehensive Python library designed for a wide range of materials imaging tasks, including image segmentation, denoising, image generation, image-to-spectrum mapping (and vice versa) and subsequent atomistic modelling of image-resolved structures. Given its broad applicability, this report aims to reproduce key aspects of the authors’ original work, extend its capabilities to new materials datasets and enhance certain features to improve model performance. We have not only successfully replicated parts of the original study, but also developed improved ML models for multiple datasets across different image processing tasks. The AtomAI library was found to be easy to use and extensible for custom applications. We believe that AtomAI holds significant potential for the microscopy and spectroscopy communities, and further development—such as semi-automated image segmentation—could broaden its utility and impact.</p>

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Reusability report: Deep learning-based analysis of images and spectroscopy data with AtomAI

  • 期刊论文

  • 发表日期：

    2025年1月2日

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  • Pragalbh Vashishtha,
  • Hitesh Gupta Kattamuri,
  • Nikhil Thawari,
  • Murugaiyan Amirthalingam,
  • Rohit Batra

摘要

Machine learning (ML) techniques are gaining traction for materials image processing applications. In this context, Ziatdinov et al. developed AtomAI, a user-friendly and comprehensive Python library designed for a wide range of materials imaging tasks, including image segmentation, denoising, image generation, image-to-spectrum mapping (and vice versa) and subsequent atomistic modelling of image-resolved structures. Given its broad applicability, this report aims to reproduce key aspects of the authors’ original work, extend its capabilities to new materials datasets and enhance certain features to improve model performance. We have not only successfully replicated parts of the original study, but also developed improved ML models for multiple datasets across different image processing tasks. The AtomAI library was found to be easy to use and extensible for custom applications. We believe that AtomAI holds significant potential for the microscopy and spectroscopy communities, and further development—such as semi-automated image segmentation—could broaden its utility and impact.