mat-defect-energy

Calculate neutral point-defect formation energies using MLIP relaxations and elemental chemical potentials.

144|21|Updated Jan 8, 2026
One-click install
npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-defect-energy
Or copy as Structured Prompt for Agent
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Skill: mat-defect-energy
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/mat-defect-energy
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-defect-energy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymatgen, pymatgen-analysis-defects, ase, numpy, json, pathlib, and includes scripts (resource) components.

What problem does it solve?

Point defects (vacancies, substitutions, and interstitials) strongly control materials properties, and computing their formation energies is a multi-step workflow that’s tedious to run manually.

Core Features & Use Cases

  • End-to-end defect energy workflow: Relaxes a bulk reference, generates defect supercells, relaxes defects with fixed cell volume, and computes formation energies using an MLIP.
  • Supports common neutral defect types: Vacancies, substitutions, and interstitials (neutral only), using symmetry-unique defect generation.
  • Chemical potential handling: Applies chemical potential corrections via elemental reference energies (metal-rich limit by default).

Quick Start

Use the mat-defect-energy skill to calculate neutral vacancy formation energies in MgO by relaxing the bulk, generating vacancy supercells, relaxing them with the cell fixed, and computing formation energies with the appropriate MLIP model and elemental references.

Frequently Asked Questions about mat-defect-energy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate point-defect formation energies using atomistic simulations?

Calculate point-defect formation energies by generating symmetry-unique defect supercells with pymatgen-analysis-defects, relaxing bulk and defective structures with an MLIP, and computing formation energies from E_defect, E_bulk, and chemical potential terms.

How do I set up chemical potentials for defect formation energy calculations?

Set up chemical potentials for defect formation energy calculations by applying elemental reference energies, typically using the metal-rich limit by default, to account for the exchange of atoms with their reservoirs.

Can I use pymatgen to generate defect supercells for vacancies and interstitials?

Yes, you can use pymatgen-analysis-defects to generate symmetry-unique defect supercells for common neutral point defect types including vacancies, substitutions, and interstitials.

What is the best way to compute vacancy formation energies with machine learning interatomic potentials?

The best way to compute vacancy formation energies with MLIPs is to relax a bulk reference supercell, generate vacancy structures, relax them with fixed cell volume, and compute formation energies using elemental reference chemical potentials.

Why do bulk and defect supercells need to share the same size for MLIP calculations?

Bulk and defect supercells must share the same fixed size to ensure commensurability, allowing accurate computation of formation energy differences by eliminating volumetric discrepancies between the relaxed reference and defective structures.

Does this defect energy workflow support charged defect states?

No, this defect energy workflow currently supports only neutral charge states for vacancies, substitutions, and interstitials, and does not compute formation energies for charged defects.