  Deep learning models were developed and implemented to aid the search for new
heavy fermion compounds. For the purpose of these calculations a database of
more than 200 heavy fermions was compiled from the literature. The deep
learning networks trained on the database were then used for regression
calculations, and predictions were made about the coherence temperature,
Sommerfeld coefficient and carrier effective mass of potential new heavy
fermions. Classification calculations were also performed in order to check
whether predicted heavy fermions are superconducting and/or antiferromagnetic.
Chemical composition was the only physical predictor used during the learning
process. Suggestions were made for future improvements in terms of expanding
the database, as well as for other artificial intelligence calculations.
