R. Elilarassi

This review surveys how machine learning and artificial intelligence, combined with high-throughput first-principles calculations and experimental databases, are used to predict key spintronic magnetic properties (such as Curie temperature, magnetocrystalline anisotropy energy, magnetic moment, spin Hall conductivity, Dzyaloshinskii–Moriya interaction, coercivity, and tunneling magnetoresistance), discusses descriptor engineering, graph neural networks, and physics-informed learning, reviews AI-assisted screening and inverse design across Heusler alloys, topological spin systems, and two-dimensional magnets, and outlines a future roadmap toward physics-guided, uncertainty-aware, and autonomous closed-loop optimization.

This review surveys how machine learning and artificial intelligence, combined with high-throughput first-principles calculations and experimental databases, are used to predict key spintronic magnetic properties (such as Curie temperature, magnetocrystalline anisotropy energy, magnetic moment, spin Hall conductivity, Dzyaloshinskii–Moriya interaction, coercivity, and tunneling magnetoresistance), discusses descriptor engineering, graph neural networks, and physics-informed learning, reviews AI-assisted screening and inverse design across Heusler alloys, topological spin systems, and two-dimensional magnets, and outlines a future roadmap toward physics-guided, uncertainty-aware, and autonomous closed-loop optimization.