magnetic-properties

name	magnetic-properties
description	Magnetic Properties (3 sub-skills: magnetic-anisotropy, magnetic-ordering, spin-polarized)

Magnetic Properties

Overview

This skill group covers first-principles calculations of magnetic properties using Quantum ESPRESSO (QE) with Python-based pre/post-processing (pymatgen, ASE, matplotlib). All workflows are designed for a Docker container environment with QE 7.5 installed.

Sub-Skills

Sub-Skill	Directory	Description
Spin-Polarized DFT	spin-polarized/	Collinear spin-polarized SCF calculations (nspin=2), magnetic moments, spin-resolved DOS, and spin density visualization via pp.x.
Magnetic Ordering	magnetic-ordering/	Comparison of FM, AFM, and non-magnetic configurations to determine ground-state magnetic order. Estimation of exchange coupling constants.
Magnetic Anisotropy	magnetic-anisotropy/	Magnetic anisotropy energy (MAE) via noncollinear DFT with spin-orbit coupling (noncolin=.true., lspinorb=.true.). Requires fully relativistic pseudopotentials. (VASPKIT 621)
Magnetic Moments	magnetic-moments/	Extract and visualize local magnetic moments. QE: Lowdin/Mulliken from projwfc.x. VASP: OUTCAR parsing. Generate MAGNETIC_MOMENTS.cif. Spin density visualization. (VASPKIT 629)

When to Use

• spin-polarized/ -- You need to check whether a material is magnetic, compute atomic magnetic moments, or obtain spin-resolved electronic structure (DOS, charge density).
• magnetic-ordering/ -- You need to determine whether a material prefers FM, AFM, or non-magnetic order, or estimate exchange coupling constants from total-energy differences.
• magnetic-anisotropy/ -- You need the magnetic anisotropy energy (easy/hard axis), which requires spin-orbit coupling and noncollinear magnetism.
• magnetic-moments/ -- You need per-atom magnetic moments, spin density maps, or a CIF file annotated with magnetic moment vectors for visualization.

General Prerequisites

• Quantum ESPRESSO 7.5 (pw.x, pp.x, projwfc.x, bands.x, dos.x)
• Python packages: pymatgen, ase, numpy, scipy, matplotlib
• Appropriate pseudopotentials (SSSP Efficiency or PseudoDojo; fully relativistic for SOC)
• Sufficient computational resources (magnetic calculations are more expensive than non-magnetic)