  Actinide and lanthanide-based materials display exotic properties that
originate from the presence of itinerant or localized f-electrons and include
unconventional superconductivity and magnetism, hidden order; and heavy fermion
behavior. Due to the strongly correlated nature of the 5f electrons, magnetic
properties of these compounds depend sensitively on applied magnetic field and
pressure, as well as on chemical doping. However, precise connection between
the structure and magnetism in actinide-based materials is currently unclear.
In this investigation, we established such structure-property links by
assembling and mining two datasets that aggregate, respectively, the results of
high-throughput DFT simulations and experimental measurements for the families
of uranium and neptunium based binary compounds. Various regression algorithms
were utilized to identify correlations among accessible attributes (features or
descriptors) of the material systems and predict their cation magnetic moments
and general forms of magnetic ordering. Descriptors representing compound
structural parameters and cation f-subshell occupation numbers were identified
as most important for accurate predictions. The best machine learning model
developed employs the Random Forest Regression algorithm and can predict
magnetic moment sizes and ordering forms in actinide-based systems with 10-20%
of root mean square error.
