chem-neb-barrier: Reusable for AI Agents | OpenAgentSkill

Overview

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

View source ↗

File metadata

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

View original text

---
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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Review before install: Avoid automatic install

License: MIT

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Financial research output is not financial advice; require human review before any live investment decision
Financial research output is not financial advice; require human review before any live investment decision.
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Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata
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Source repository: learningmatter-mit/AtomisticSkills
License: MIT
Version: 1.0.0
Last GitHub push: Sep 3, 2026
Registry updated: Sep 6, 2026
Instruction path: .agents/skills/chem-neb-barrier/SKILL.md @ d1f7ecfda2cd

Version reported in registry metadata; check source releases before relying on it.

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69/100

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    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-neb-barrier"
  }
}

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Creator: learningmatter-mit
Source: learningmatter-mit/AtomisticSkills
Indexed by: OpenAgentSkill community index

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-neb-barrier?metric=listed&label=Listed)](https://www.openagentskill.com/skills/learningmatter-mit-chem-neb-barrier?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-neb-barrier?metric=trust&label=Trust)](https://www.openagentskill.com/skills/learningmatter-mit-chem-neb-barrier?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-neb-barrier?metric=audit&label=Audit)](https://www.openagentskill.com/skills/learningmatter-mit-chem-neb-barrier/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-neb-barrier?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/learningmatter-mit-chem-neb-barrier?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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