Structural Characterization

Structural characterization aims to determine the physical arrangement and properties of materials and systems, often using multiple complementary techniques. Current research focuses on developing efficient algorithms and models, including graph neural networks, convolutional neural networks, and recurrent neural networks, to analyze complex data from various sources like microscopy images, sensor readings, and scattering patterns. These advancements enable faster, more accurate analysis and improved anomaly detection, impacting fields ranging from materials science and structural engineering to astrophysics and software development. The integration of machine learning with traditional methods enhances both the speed and accuracy of structural characterization across diverse disciplines.