High Performance Computational Multi-Scale Framework for the Discovery of Promising Magnetocaloric Materials

Guy Moore, University of California, Berkeley

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Moving from the atomistic picture of magnetism to larger length scale models is an important challenge for the design and discovery of promising candidates for material science and physics. This problem requires increased computational demand and care for correlated electron systems, such as transition metal oxides, in which multi-body interactions are difficult to model using conventional Kohn–Sham density functional theory (DFT). In this study, we present a framework for obtaining magnetic exchange constants from DFT+U+J using the established single-particle Green’s function approach, which can be used to study finite-temperature behavior of lattice models using Monte Carlo methods. The Heisenberg exchange constants are highly sensitive to two important prerequisites: the magnetic ground-state, as well as the Hubbard U and Hund J values in DFT+U+J, which parameterize on-site corrections to coulomb interactions between localized electrons. We explore the sensitivity of the magnetic ground state and resulting exchange constants to U and J values. These Hubbard U and Hund J values are computed using the linear response formalism suitable for high throughput DFT applications. Additionally, we have implemented constraints within conventional spin-DFT that result in an effective current-density functional with improved convergence to the experimentally measured ground-state. This ground-up computational approach will allow for the discovery of magnetic materials with technological applications ranging from spintronics to cost-effective magnetocaloric materials for magnetic refrigeration.

Abstract Author(s): Guy C. Moore, Matthew K. Horton, Kristin A. Persson