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mat-grain-boundary

Calculate grain boundary energies and identify low-energy CSL boundaries using MLIP relaxation.

144|21|Updated Jan 8, 2026
Search Tags:#materials modeling#grain boundary#interatomic potentials#CSL enumeration#energy analysis#MLIP relaxation#DFT-comparison
One-click install
npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-grain-boundary
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mat-grain-boundary
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/mat-grain-boundary
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-grain-boundary

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

It calculates grain boundary energies for CSL tilt/twist grain boundaries and produces energy-vs-misorientation curves so you can identify low-energy “special” boundaries for materials modeling.

Core Features & Use Cases

  • CSL grain boundary generation: Enumerates Σ boundaries and produces grain-boundary supercell CIFs for a chosen rotation axis and angle range.
  • MLIP-based relaxation workflow: Relaxes bulk and grain boundary structures using the same ML foundation potential, enforcing relax_cell=False for GBs to preserve CSL geometry.
  • Energy computation and analysis: Computes γ_GB via ( \gamma_{GB} = \frac{E_{GB} - N E_{bulk}}{2A} ), exports results (JSON/CSV) and generates plots for cusp identification.

Quick Start

Run the full workflow to generate Σ grain boundary structures, relax them with the same MLIP model, and compute γ_GB vs misorientation angle for a selected axis (e.g., [001]) using the provided scripts and outputs.

Frequently Asked Questions about mat-grain-boundary

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

FAQPage Schema
How do I calculate grain boundary energy using machine learning interatomic potentials?▼

Generating grain boundary energy vs misorientation curves involves enumerating Σ boundaries for a rotation axis, relaxing supercells with MLIPs, and plotting γ_GB to identify low-energy cusps. This workflow exports results as JSON/CSV and plots for special boundary identification.

Why does grain boundary relaxation need to preserve CSL in-plane lattice vectors?▼

Identifying low-energy special CSL boundaries requires computing γ_GB across a Σ range for selected rotation axes and looking for cusps in the energy-vs-misorientation curve. Σ3 twins and related cusps are typical low-energy special boundaries found this way.

Can I compare grain boundary energies from MLIPs against DFT results?▼

Yes, MLIP-based grain boundary energies can be compared against DFT results. The workflow requires consistent bulk and GB energy evaluation using identical MLIP models to ensure the γ_GB values are directly comparable to DFT reference data.

What file formats are exported for grain boundary energy analysis?▼

Grain boundary energy analysis exports results in JSON and CSV formats, alongside generated plots for cusp identification in the energy-vs-misorientation curves. Supercell structures are produced as CIF files for visualization and further simulation.

Does the grain boundary energy calculation require external interatomic potential libraries?▼

The grain boundary energy calculation requires a machine learning interatomic potential model to relax both bulk and GB structures, but lists no external library dependencies. Using identical MLIP models for bulk and GB evaluation ensures consistent energy references.

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