What is chem-neb-barrier?

A calculation of the energy barrier between two atomic arrangements using the Nudged Elastic Band method. The method estimates the highest-energy part of an atom movement or chemical reaction using machine-learning models of atomic forces.

Use it to calculate migration barriers in solid materials or transition-state barriers for molecules from two endpoint structures.

It helps estimate how difficult it is for atoms to move or for a reaction to occur between a starting and ending structure.

chem-neb-barrier — Skill for Claude Code and Codex by learningmatter-m

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add learningmatter-mit/AtomisticSkills --skill chem-neb-barrier

Clone the repo

git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/chem-neb-barrier.svg)](https://agentmods.dev/skills/learningmatter-mit/atomisticskills/chem-neb-barrier)

Your own site

<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/chem-neb-barrier"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/chem-neb-barrier.svg" alt="Measured on agentmods" height="20"></a>

Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,385 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00023	$0.01385
Opus 5	$0.00012	$0.00692
Sonnet 5	$0.00005	$0.00277
Haiku 4.5	$0.00002	$0.00138

Measured 7d ago against content hash e0029aa73357, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

chem-neb-barrier scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (examples/butane_conformer/run_example.py, examples/LiCoO2/prepare_licoo2.py, examples/LiCoO2/run_example.sh, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/chem-neb-barrier/SKILL.md · 135 lines

How it starts

The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 135 lines

Files

What ships with it

17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

7d ago First seen · 135 lines · 23 tokens per session scan A e0029aa73357

Subscribe to this mod's changes

chem-neb-barrier is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (161 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 1,385 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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