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chem-neb-barrier

Calculate activation barrier using Nudged Elastic Band (NEB) method with MLIPs.

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Calculate activation barrier using Nudged Elastic Band (NEB) method with MLIPs.

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NEB Barrier Calculation

This skill calculates the activation energy barrier for atomic migration or chemical reaction transition states using the Nudged Elastic Band (NEB) method with Machine Learning Interatomic Potentials (MLIPs).

Supports both:

Materials: solid-state diffusion barriers (periodic systems)
Chemistry: molecular transition states (non-periodic systems)

The script auto-detects periodic boundary conditions from the input structures.

Tools

1. `calculate_barrier.py`

Performs the NEB calculation between two endpoint structures.

Usage:

Use with MACE (periodic materials)

# Env: mace-agent
python .agents/skills/chem-neb-barrier/scripts/calculate_barrier.py \
    --start_structure <path_to_start.cif> \
    --end_structure <path_to_end.cif> \
    --model_type mace \
    --n_images 5 \
    --fmax 0.05 \
    --output_dir <output_directory>

Use with MACE (non-periodic molecules)

# Env: mace-agent
python .agents/skills/chem-neb-barrier/scripts/calculate_barrier.py \
    --start_structure reactant.xyz \
    --end_structure product.xyz \
    --model_type mace \
    --model_name MACE-OFF23-small \
    --n_images 7 \
    --fmax 0.05 \
    --output_dir <output_directory>

Use with FairChem

# Env: fairchem-agent
python .agents/skills/chem-neb-barrier/scripts/calculate_barrier.py \
    --start_structure <path_to_start.cif> \
    --end_structure <path_to_end.cif> \
    --model_type fairchem \
    --n_images 5 \
    --fmax 0.05 \
    --output_dir <output_directory>

Use with MatGL

# Env: matgl-agent
python .agents/skills/chem-neb-barrier/scripts/calculate_barrier.py \
    --start_structure <path_to_start.cif> \
    --end_structure <path_to_end.cif> \
    --model_type matgl \
    --n_images 5 \
    --fmax 0.05 \
    --output_dir <output_directory>

Arguments:

--start_structure: Path to the initial stable structure (CIF/POSCAR/XYZ).
--end_structure: Path to the final stable structure (CIF/POSCAR/XYZ).
--model_type: Type of MLIP to use (mace, fairchem, matgl).
--model_name: Specific model name/path (optional, uses default if not specified).
--model_head: Model head for multi-head models (e.g., omat, omol for UMA; omat_pbe, matpes_r2scan for MACE-MH).
--n_images: Number of intermediate images (default: 7).
--fmax: Force convergence criterion in eV/Å (default: 0.02).
--interpolation: Method for initial path generation. Options: linear, idpp (default). Recommended to use idpp for dense systems.
--climb: Use Climbing Image NEB (CI-NEB) (default: True).
--output_dir: Directory to save results and plots.

Outputs:

neb_trajectory.traj: ASE trajectory of the optimized path.
neb_barrier_plot.png: Plot of energy vs reaction coordinate.
neb_results.json: JSON file containing barrier energy and forces.
neb_path.cif (periodic) or neb_path.xyz (non-periodic): Path structures.

Model Recommendations

Periodic Materials (solid-state diffusion)

Recommended:
- OMAT:
  - MACE-OMAT-0-small
  - MACE-MH-1 (head: omat_pbe)
  - uma-s-1p1 (head: omat)
- MatPES:
  - MACE-MATPES-r2SCAN-0
  - MACE-MH-1 (head: matpes_r2scan)
  - CHGNet-MatPES-r2SCAN-2025.2.10-2.7M-PES
  - TensorNet-MatPES-r2SCAN-v2025.1-PES
- These models are trained on datasets including transition states or diverse structures (OMat24, MatPES), making them more reliable for NEB.
Discouraged:
- MPtrj trained models (e.g., M3GNet-MP-2021, CHGNet-MPtrj-2023.12.1-2.7M-PES)
- These are primarily trained on ground-state or near-equilibrium structures and may underestimate barriers or fail to converge for high-energy transition states.

Non-periodic Molecules (transition states)

Recommended:
- MACE-OFF23-small / MACE-OFF23-medium — trained on organic molecules
- uma-s-1p1 (head: omol) — general molecular model
- MACE-MH-1 (head: omol) — multi-head molecular model

Prerequisites

Ensure the appropriate environment is active for the chosen model type (see mcp_config.json).
CRITICAL: The start and end structures MUST be pre-relaxed using the same MLIP model used for the NEB calculation.
- For periodic: Use relax_cell=False (fixed volume) to ensure consistency between endpoints.
- For non-periodic: Ensure pbc=False is set on the structures.
- Use fmax=0.02 eV/Å for tight convergence.
- You can use the relax_structure tool from the corresponding MCP server for this.

Examples

See `examples/` directory for sample inputs and outputs.

Author: Bowen Deng Contact: GitHub @learningmatter-mit

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Métadonnées du fichier

name: chem-neb-barrier
description: Calculate activation barrier using Nudged Elastic Band (NEB) method with MLIPs.
category: [chemistry, materials]

Voir le texte original

---
name: chem-neb-barrier
description: Calculate activation barrier using Nudged Elastic Band (NEB) method with MLIPs.
category: [chemistry, materials]
---

# NEB Barrier Calculation

This skill calculates the activation energy barrier for atomic migration or chemical reaction transition states using the Nudged Elastic Band (NEB) method with Machine Learning Interatomic Potentials (MLIPs).

Supports both:
- **Materials**: solid-state diffusion barriers (periodic systems)
- **Chemistry**: molecular transition states (non-periodic systems)

The script auto-detects periodic boundary conditions from the input structures.

## Tools

### 1. `calculate_barrier.py`

Performs the NEB calculation between two endpoint structures.

**Usage:**

### Use with MACE (periodic materials)
```bash
# Env: mace-agent
python .agents/skills/chem-neb-barrier/scripts/calculate_barrier.py \
    --start_structure <path_to_start.cif> \
    --end_structure <path_to_end.cif> \
    --model_type mace \
    --n_images 5 \
    --fmax 0.05 \
    --output_dir <output_directory>
```

### Use with MACE (non-periodic molecules)
```bash
# Env: mace-agent
python .agents/skills/chem-neb-barrier/scripts/calculate_barrier.py \
    --start_structure reactant.xyz \
    --end_structure product.xyz \
    --model_type mace \
    --model_name MACE-OFF23-small \
    --n_images 7 \
    --fmax 0.05 \
    --output_dir <output_directory>
```

### Use with FairChem
```bash
# Env: fairchem-agent
python .agents/skills/chem-neb-barrier/scripts/calculate_barrier.py \
    --start_structure <path_to_start.cif> \
    --end_structure <path_to_end.cif> \
    --model_type fairchem \
    --n_images 5 \
    --fmax 0.05 \
    --output_dir <output_directory>
```

### Use with MatGL
```bash
# Env: matgl-agent
python .agents/skills/chem-neb-barrier/scripts/calculate_barrier.py \
    --start_structure <path_to_start.cif> \
    --end_structure <path_to_end.cif> \
    --model_type matgl \
    --n_images 5 \
    --fmax 0.05 \
    --output_dir <output_directory>
```

**Arguments:**
- `--start_structure`: Path to the initial stable structure (CIF/POSCAR/XYZ).
- `--end_structure`: Path to the final stable structure (CIF/POSCAR/XYZ).
- `--model_type`: Type of MLIP to use (`mace`, `fairchem`, `matgl`).
- `--model_name`: Specific model name/path (optional, uses default if not specified).
- `--model_head`: Model head for multi-head models (e.g., `omat`, `omol` for UMA; `omat_pbe`, `matpes_r2scan` for MACE-MH).
- `--n_images`: Number of intermediate images (default: 7).
- `--fmax`: Force convergence criterion in eV/Å (default: 0.02).
- `--interpolation`: Method for initial path generation. Options: `linear`, `idpp` (default). **Recommended** to use `idpp` for dense systems.
- `--climb`: Use Climbing Image NEB (CI-NEB) (default: True).
- `--output_dir`: Directory to save results and plots.

**Outputs:**
- `neb_trajectory.traj`: ASE trajectory of the optimized path.
- `neb_barrier_plot.png`: Plot of energy vs reaction coordinate.
- `neb_results.json`: JSON file containing barrier energy and forces.
- `neb_path.cif` (periodic) or `neb_path.xyz` (non-periodic): Path structures.

## Model Recommendations

### Periodic Materials (solid-state diffusion)

- **Recommended**:
    - **OMAT**:
        - `MACE-OMAT-0-small`
        - `MACE-MH-1` (head: `omat_pbe`)
        - `uma-s-1p1` (head: `omat`)
    - **MatPES**:
        - `MACE-MATPES-r2SCAN-0`
        - `MACE-MH-1` (head: `matpes_r2scan`)
        - `CHGNet-MatPES-r2SCAN-2025.2.10-2.7M-PES`
        - `TensorNet-MatPES-r2SCAN-v2025.1-PES`
    - These models are trained on datasets including transition states or diverse structures (OMat24, MatPES), making them more reliable for NEB.

- **Discouraged**:
    - **MPtrj** trained models (e.g., `M3GNet-MP-2021`, `CHGNet-MPtrj-2023.12.1-2.7M-PES`)
    - These are primarily trained on ground-state or near-equilibrium structures and may underestimate barriers or fail to converge for high-energy transition states.

### Non-periodic Molecules (transition states)

- **Recommended**:
    - `MACE-OFF23-small` / `MACE-OFF23-medium` — trained on organic molecules
    - `uma-s-1p1` (head: `omol`) — general molecular model
    - `MACE-MH-1` (head: `omol`) — multi-head molecular model

## Prerequisites

- Ensure the appropriate environment is active for the chosen model type (see `mcp_config.json`).
- **CRITICAL**: The start and end structures **MUST** be pre-relaxed using the *same* MLIP model used for the NEB calculation.
    - For periodic: Use `relax_cell=False` (fixed volume) to ensure consistency between endpoints.
    - For non-periodic: Ensure `pbc=False` is set on the structures.
    - Use `fmax=0.02` eV/Å for tight convergence.
    - You can use the `relax_structure` tool from the corresponding MCP server for this.

## Examples

See `examples/` directory for sample inputs and outputs.
---

**Author:** Bowen Deng
**Contact:** [GitHub @learningmatter-mit](https://github.com/learningmatter-mit)

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Dépôt source: learningmatter-mit/AtomisticSkills
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 3 sept. 2026
Registre mis à jour: 6 sept. 2026
Chemin des instructions: .agents/skills/chem-neb-barrier/SKILL.md @ d1f7ecfda2cd

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

66/100

Prometteur

Confiance

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Sandbox uniquement

Audit

75/100

Revue nécessaire

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Dependency or permission surface needs review
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Financial research output is not financial advice; require human review before any live investment decision
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Quality score needs review
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Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata
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    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 70/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "learningmatter-mit-chem-neb-barrier (chem-neb-barrier)",
      "install_command": "npx skills add learningmatter-mit/AtomisticSkills --skill chem-neb-barrier",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "learningmatter-mit-chem-neb-barrier",
      "task": "Use chem-neb-barrier in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/learningmatter-mit-chem-neb-barrier",
    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-neb-barrier",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-neb-barrier/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-neb-barrier&task=Use%20chem-neb-barrier%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-neb-barrier%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-neb-barrier%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-neb-barrier/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-neb-barrier"
  }
}

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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: learningmatter-mit
Source: learningmatter-mit/AtomisticSkills
Indexé par: Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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