mat-diffusion-analysis

mat-diffusion-analysis is a skill for Claude Code, Codex from learningmatter-mit/AtomisticSkills. It costs 21 tokens per session (975 once invoked), scanned A, original, MIT.

Calculate ionic diffusion coefficients and activation energy from MD trajectories using pymatgen.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/learningmatter-mit/atomisticskills/mat-diffusion-analysis
Any agent
npx skills add learningmatter-mit/AtomisticSkills --skill mat-diffusion-analysis
Clone the repo
git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills

Made for: Claude Code, Codex.

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README.md
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<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-diffusion-analysis"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-diffusion-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 975 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown 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.

ModelPer sessionOnce invoked
Fable 5 $0.00021 $0.00975
Opus 5 $0.00010 $0.00487
Sonnet 5 $0.00004 $0.00195
Haiku 4.5 $0.00002 $0.00097

Measured today against content hash 5eea08823da3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mat-diffusion-analysis 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 today.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/analyze_diffusion.py, scripts/calculate_activation_energy.py), 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/mat-diffusion-analysis/SKILL.md · 69 lines

How it starts

The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Diffusion Analysis

Goal

To accurately calculate the ionic diffusivity ($D$) and activation energy ($E_a$) of specific atomic species in a material using Molecular Dynamics (MD) trajectories and the Arrhenius relation: $D(T) = D_0 \exp\left(-\frac{E_a}{k_B T}\right)$.

Instructions

  1. MD Preparation: Run NVT or NPT MD simulations at multiple temperatures (typically 4-6 points between 600K and 1200K).

    • Use the run_md tool from a relevant potential skill (e.g., mace or matgl).
    • Batch Processing: You can pass a directory or a list of CIF paths to structure_data to run multiple MD simulations concurrently via the MCP tool.
    • Supercell Expansion: Ensure supercells are sufficiently large (> 10 Å in all dimensions). The run_md tool natively supports this via the supercell_min_length argument (defaults to 10.0 Å) which performs orthogonal expansion automatically.
    • Optimization: Use the diffusion monitor (see mat-md-monitors) to automatically stop simulations once the transport properties have converged.
      mace.run_md(
          structure_data=["candidates/A.cif", "candidates/B.cif"],
          temperature=600,
          supercell_min_length=10.0,
          monitor=True,
          monitor_type="diffusion",
          monitor_params={"specie": "Li", "threshold": 0.05, "check_interval_ps": 5.0}
      )
      
      Note: If the diffusion monitor triggers an early stop, it will automatically save the diffusion_{specie}.json and msd_{specie}.png directly into the trajectory output directory. You can skip Step 2 and proceed directly to Step 3 for any trajectories that converged early.
  2. Individual Diffusivity Analysis: For each temperature directory that did not hit the early stopping criteria, run the analysis script to extract the diffusivity and Mean Square Displacement (MSD).

    # Env: base-agent
    python .agents/skills/mat-diffusion-analysis/scripts/analyze_diffusion.py \
        results/md_600K/trajectory.traj \
        --species Li \
        --temperature 600 \
        --ignore_ps 5.0 \
        --output_dir results/md_600K
    
    • --ignore_ps: Time to skip for equilibration. Default is 5.0 ps.
    • The script automatically detects the frame interval from the .log file if present.

Read the full file on GitHub · 69 lines

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.

  1. today First seen · 69 lines · 21 tokens per session scan A 5eea08823da3

Subscribe to this mod's changes

mat-diffusion-analysis is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 975 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 2026-09-03.

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