A. Fouda

It is demonstrated that the physical connection, and thus inter-task relationship, between the generation of a core-hole and its corresponding Auger-Meitner decay enables inductive knowledge transfer through the training of a multi-task graph neural network to predict both observables from a common graph embedding.

The wealth of information encoded in the Auger spectrum offers the opportunity to train convolutional neural networks for site-selective functional group classification in organic molecules directly from the Auger spectrum lineshape, demonstrating the potential for new data-driven characterization capabilities in spectroscopic techniques that are information-rich but difficult to interpret.