Searching for new heavy fermions with deep learning

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.