mat-phase-field-non-conservative
Simulate non-conservative phase-fields (grain growth and phase transformations) using the Allen-Cahn equation.
Skill by learningmatter-mit
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Use cases: marketing
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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
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- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/mat-phase-field-non-conservative
What's inside
- Skill name
mat-phase-field-non-conservative- Description
- Simulate non-conservative phase-fields (grain growth and phase transformations) using the Allen-Cahn equation.
- License
- MIT
- Instructions
- 61 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (8)
- SKILL.md
- README.md · examples/benchmark-dendrite/
- dendrite.gif · examples/benchmark-dendrite/
- README.md · examples/benchmark-grain/
- grain_growth.gif · examples/benchmark-grain/
- grain_growth_area_decay.png · examples/benchmark-grain/
- run_dendrite_growth.py · scripts/
- run_grain_growth.py · scripts/
SKILL.md (first lines)
# Non-Conservative Phase-Field: Allen-Cahn
## Goal
To simulate the morphological evolution of structural transformations (like solidification, melting, or curvature-driven grain growth) using the Allen-Cahn (time-dependent Ginzburg-Landau) equation. This tracks a non-conservative order parameter $\phi$ which distinguishes between phases (e.g., solid vs. liquid).
## Instructions
### 1. Mathematical Formulation
The Allen-Cahn equation describes the evolution of a non-conserved order parameter $\phi$ down a free energy gradient:
$$ \frac{\partial \phi}{\partial t} = -M \frac{\delta F}{\delta \phi} = M \left( \epsilon^2 \nabla^2 \phi - \frac{\partial f(\phi)}{\partial \phi} \right) $$
Where $M$ is the mobility, $\epsilon$ is the gradient energy coefficient controlling the interface thickness, and $f(\phi) = W \phi^2(1-\phi)^2$ is the double-well potential barrier between the two phases ($\phi=0$ and $\phi=1$).
Unlike Cahn-Hilliard, Allen-Cahn does not conserve the integral of $\phi$. It naturally drives systems to reduce their total interfacial area, resulting in curvature-driven boundary migration.
### 2. Running Curvature-Driven Grain Growth
Use the provided script to set up a 2D grid containing a circular solid grain in a liquid matrix and observe its capillarity-driven shrinkage.
```bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/run_grain_growth.py \
--grid-size 100 \
--radius 30 \
--steps 200 \
--dt 0.1 \
--output grain_growth.gif
```
**Parameters:**
- `--grid-size`: Number of grid points per dimension (e.g., `100` for a 100x100 2D grid).
- `--radius`: Initial radius of the circular grain in grid units.
- `--steps`: Total number of time steps to run.
- `--dt`: Time step size.
- `--output`: Filepath to save the resulting `.gif` animation or `.png`.
## Examples
### Classic Shrinking Circular Grain
A universal mathematical benchmark for the Allen-Cahn equation is proving that a circular domain shrinks at a rate proportional to its curvature (the $v = M \gamma K$ law). The area of the circle must decrease linearly with time.
```bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/run_grain_growth.py \
--grid-size 100 \
--radius 35 \
--steps 300 \
--dt 0.5 \
--output examples/benchmark-grain/classic_shrinking_grain.gif
```
See the `examples/benchmark-grain/README.md` for the expected output.
## Constraints
- **Environments**: Scripts require the `cpu` environment. **Each code block MUST specify the environment.**
- **Interface Thickness**: The spatial resolution `dx` must be small enough to resolve the diffuse interface (typically requiring at least 4-5 grid points across the interface controlled by $\epsilon$).
## References
- Allen, S. M., & Cahn, J. W., "A macroscopic theory for antiphase boundary motion and its application to antiphase domain coarsening", *Acta Metallurgica*, 1979. [DOI](https://doi.org/10.1016/0001-6160(79)90196-2)
---
**Author:** Bowen DengFrom learningmatter-mit/AtomisticSkills, MIT.
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Signals
- Stars
- 183
- Forks
- 31
- Open issues
- 0
- Last commit (this folder)
- Oct 5, 2026 (1 month ago)
- Last release
- Oct 6, 2026 (29 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Website
- arxiv.org/abs/2605.24002
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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mat-phase-field-non-conservative skill. Agent Mart. https://agentmart.dev/skills/learningmatter-mit-mat-phase-field-non-conservative (last updated 2026-10-24)