  In the wake of the growing popularity of machine learning in particle
physics, this work finds a new application of geometric deep learning on
Feynman diagrams to make accurate and fast matrix element predictions with the
potential to be used in analysis of quantum field theory. This research uses
the graph attention layer which makes matrix element predictions to 1
significant figure accuracy above 90% of the time. Peak performance was
achieved in making predictions to 3 significant figure accuracy over 10% of the
time with less than 200 epochs of training, serving as a proof of concept on
which future works can build upon for better performance. Finally, a procedure
is suggested, to use the network to make advancements in quantum field theory
by constructing Feynman diagrams with effective particles that represent
non-perturbative calculations.
