Orbital fingerprinting of magnetism across the Mn-Ni-Ga Heusler ternary
Abstract
In Mn--Ni--Ga Heusler alloys, the competing magnetic states are separated by differences of a few meV per atom, so the ground state has to be resolved from the electronic structure and cannot be read off the composition. Moving away from the stoichiometric compounds, where the established rules for Heusler magnetism fall short, increases this difficulty. To overcome it, we introduce orbital fingerprints taken from the projected density of states, and use them as the input to machine-learning models for classification of the magnetic ordering, for the magnetic moment amplitude, for the spin polarization at the Fermi level and for interpolation of the phase diagram. The models are trained on a dataset of 370 spin-polarized first-principles calculations of quasirandom structures covering the ternary, which show good agreement with the magnetic ground states and lattice parameters available in the literature. For magnetic ordering, the top-ranked descriptor is the same quantity found by first-principles calculations to distinguish the phases, suggesting that the fingerprints capture the underlying physics.
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BibTeX
@misc{hawthorne2026orbital,
title = {{Orbital fingerprinting of magnetism across the Mn-Ni-Ga Heusler ternary}},
author = {Felipe Hawthorne and Daniela A. Damasceno and Raphael M. Tromer and Ronaldo R. Pelá and Cristiano F. Woellner},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.19835},
eprint = {2609.19835},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.19835},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Orbital fingerprinting of magnetism across the Mn-Ni-Ga Heusler ternary
AU - Felipe Hawthorne
AU - Daniela A. Damasceno
AU - Raphael M. Tromer
AU - Ronaldo R. Pelá
AU - Cristiano F. Woellner
PY - 2026
DA - 2026/09/17
PB - arXiv
DO - 10.48550/arxiv.2609.19835
UR - https://arxiv.org/abs/2609.19835
AB - In Mn--Ni--Ga Heusler alloys, the competing magnetic states are separated by differences of a few meV per atom, so the ground state has to be resolved from the electronic structure and cannot be read off the composition. Moving away from the stoichiometric compounds, where the established rules for Heusler magnetism fall short, increases this difficulty. To overcome it, we introduce orbital fingerprints taken from the projected density of states, and use them as the input to machine-learning models for classification of the magnetic ordering, for the magnetic moment amplitude, for the spin polarization at the Fermi level and for interpolation of the phase diagram. The models are trained on a dataset of 370 spin-polarized first-principles calculations of quasirandom structures covering the ternary, which show good agreement with the magnetic ground states and lattice parameters available in the literature. For magnetic ordering, the top-ranked descriptor is the same quantity found by first-principles calculations to distinguish the phases, suggesting that the fingerprints capture the underlying physics.
ER -