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We show that the Gaussian Approximation Potential machine learning frameworkcan describe complex magnetic potential energy surfaces, taking ferromagneticiron as a paradigmatic challenging case. The training database includes totalenergies, forces, and stresses obtained from density-functional theory in thegeneralized-gradient approximation, and comprises approximately 150,000 localatomic environments, ranging from pristine and defected bulk configurations tosurfaces and generalized stacking faults with different crystallographicorientations. We find the structural, vibrational and thermodynamic propertiesof the GAP model to be in excellent agreement with those obtained directly fromfirst-principles electronic-structure calculations. There is goodtransferability to quantities, such as Peierls energy barriers, which aredetermined to a large extent by atomic configurations that were not part of thetraining set. We observe the benefit and the need of using highly convergedelectronic-structure calculations to sample a target potential energy surface.The end result is a systematically improvable potential that can achieve thesame accuracy of density-functional theory calculations, but at a fraction ofthe computational cost.

Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron