Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems

1 code implementation • 4 Feb 2022 • Massimiliano Lupo Pasini, Pei Zhang, Samuel Temple Reeve, Jong Youl Choi

We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with a fixed body centered tetragonal (BCT) lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range.

Multi-Task Learning