Magnetic Moments

A research topic across the group’s papers.

The local magnetic moment on an atom (in Bohr magnetons, µB) and the way those moments are arranged across a structure (non-magnetic, ferromagnetic, ferrimagnetic or antiferromagnetic collinear orderings) define a material’s magnetic state. In spin-polarized DFT the result depends on the starting guess: codes such as VASP need an initial moment for every atom (the MAGMOM vector), and the electronic minimization tends to find a nearby local solution. Practitioners therefore often try several orderings and pick the lowest in energy, but the magnitude of the initial moments is usually set by heuristics or chemical intuition, and the number of possible orderings grows combinatorially with system size. Machine learning surrogates that predict per-atom moments offer a way to screen magnetic materials and to seed DFT calculations. See also graph neural networks and imbalanced data.

Per-atom moment prediction with a two-part GNN (2024)

Sanspeur and Kitchin trained graph neural networks on antiferromagnetic ground-state structures from the Materials Project, where about 90% of atomic moments are near zero. A GemNet-OC classifier first flags atoms with moments above 0.1 µB (F1 and MCC about 91%), and a regressor on its embeddings then predicts the moment magnitude with a mean absolute error of 0.1 µB. The embeddings could not distinguish spin-up from spin-down atoms, so only magnitudes are predicted. Used as the MAGMOM starting guess for the bulk oxide Mo2Ru13PtO32, the predicted moments reached the electronic ground state about 30% faster and about 0.6 eV lower in energy than uniform 5 µB or 0.6 µB starts (Sanspeur 2024). Summary: sanspeur2024.

Papers on this topic