mat-defect-energy-dft

mat-defect-energy-dft is a skill for Claude Code, Codex from learningmatter-mit/AtomisticSkills. It costs 31 tokens per session (1,440 once invoked), scanned A, original, MIT.

A DFT workflow for calculating charged point-defect formation energies and charge-transition diagrams. DFT, or density functional theory, is a quantum-mechanical method for estimating material energies; a transition diagram shows which defect charge is favored at different Fermi levels.

In plain words
What is it for?
Generating defect structures, running VASP calculations, applying corrections, and producing formation-energy diagrams for vacancies, substitutions, and interstitials.
Why use it?
It includes charge states and finite-size corrections that are needed for more realistic defect-energy comparisons.

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-defect-energy-dft
Any agent
npx skills add learningmatter-mit/AtomisticSkills --skill mat-defect-energy-dft
Clone the repo
git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills

Made for: Claude Code, Codex.

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README.md
[![agentmods](https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-defect-energy-dft.svg)](https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-defect-energy-dft)
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<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-defect-energy-dft"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-defect-energy-dft.svg" alt="Measured on agentmods" height="20"></a>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,440 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00031 $0.01440
Opus 5 $0.00015 $0.00720
Sonnet 5 $0.00006 $0.00288
Haiku 4.5 $0.00003 $0.00144

Measured yesterday against content hash 284bfdeb80f5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mat-defect-energy-dft 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 yesterday.

The scan reads SKILL.md. This mod also ships 3 executable files (examples/MgO_charged_vacancy/build_diagram.py, scripts/generate_defect_structures.py, scripts/parse_defect_results.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-defect-energy-dft/SKILL.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Point-Defect Formation Energy (DFT)

Goal

To calculate the formation energy of point defects (vacancies, substitutions, interstitials) including charged defect states and finite-size corrections using DFT (VASP) via atomate2 workflows. This produces formation energy diagrams showing defect charge transition levels as a function of Fermi energy.

$$E_f[D^q] = E[D^q] - E[\text{bulk}] + \sum_i \Delta n_i \mu_i + q(E_\text{VBM} + \Delta E_F) + E_\text{corr}$$

where $q$ is the charge state, $E_\text{VBM}$ is the valence band maximum, $\Delta E_F$ is the Fermi energy relative to VBM, and $E_\text{corr}$ is the finite-size correction (Freysoldt/FNV).

Instructions

1. Obtain Bulk Structure

Start with a relaxed primitive cell:

mcp_base_search_materials_project_by_formula(formula="MgO", save_to_file="MgO.cif")

2. Generate Defect Structures

Use pymatgen-analysis-defects to generate all symmetry-unique defect supercells with charge states:

# Env: base-agent
python .agents/skills/mat-defect-energy-dft/scripts/generate_defect_structures.py \
    --bulk MgO.cif \
    --supercell_size 3 3 3 \
    --defect_type vacancy \
    --charge_range -2 2 \
    --output dft_defects/

This generates:

  • POSCAR files for each defect × charge state
  • A defect_index.json mapping defect names to charge states and structures
  • Pristine supercell for the bulk reference

3. Run DFT Calculations (atomate2)

Submit calculations via the atomate2 MCP tool:

# Bulk supercell reference
mcp_atomate2_run_atomate2_vasp_calculation(
    structures_path="dft_defects/pristine_supercell.cif",
    output_dir="./dft_bulk/",
    calculation_type="static",
    preset_type="matpes-pbe",
    execution_mode="remote"
)

# All defect structures
mcp_atomate2_run_atomate2_vasp_calculation(
    structures_path="dft_defects/",
    output_dir="./dft_defect_calcs/",
    calculation_type="relaxation",
    preset_type="matpes-pbe",
    execution_mode="remote"
)

Read the full file on GitHub · 142 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. yesterday First seen · 142 lines · 31 tokens per session scan A 284bfdeb80f5

Subscribe to this mod's changes

mat-defect-energy-dft is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 1,440 once invoked, about $0.0002 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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