Agent skill

magnetic-properties

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/magnetic-properties

SKILL.md

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)

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