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A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Engineering and Technology

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

L. R. Dacosta, K. Sytwu, et al.

Explore how the innovative Python package Construction Zone enables the generation of complex nanoscale atomic structures, significantly enhancing the creation of diverse synthetic datasets for training machine learning models to analyze HRTEM images. This groundbreaking research from Luis Rangel DaCosta, Katherine Sytwu, C. K. Groschner, and M. C. Scott achieves state-of-the-art nanoparticle image segmentation using solely simulated data.

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