Understanding Magnetic Properties of Actinide-Based Compounds from Machine Learning

Abstract

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.

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Cited by in corpus (3)

• Insights into cation ordering of double perovskite oxides from machine learning and causal relations
• Classification of magnetic order from electronic structure by using machine learning
• Structure-Driven Prediction of Magnetic Order in Uranium Compounds