Our Research and Development Lead Scientist Antony Vamvakeros gave a webinar on chemical tomography and neural networks on the 20th May hosted by the Neel Institut
Synchrotron X-ray chemical tomography methods combine a scattering or spectroscopic technique with a tomographic data acquisition approach. These non-destructive methods yield a cross-section of the studied sample where each pixel in the reconstructed images corresponds to a chemical signal (e.g. X-ray diffraction pattern or spectrum). These spatially-resolved signals most often reveal information that is lost in conventional bulk measurements. In this talk, he briefly introduces X-ray diffraction computed tomography (XRD-CT) and presents a few examples where we have applied such methods to track the evolving solid-state chemistry of complex functional materials and devices under operating conditions. In the second part of the webinar, he focuses on our latest technical advances regarding processing and analysis of these large and rich chemical imaging datasets using deep learning methods with neural networks.
Watch a recording of the webinar below: