dft-defects

dft-defects is a skill for Claude Code, Codex from SFETNI/Deep-Matter-Chem-Skills. It costs 4 tokens per session (13,457 once invoked), scanned A, original, MIT.

A guide for modeling point defects, such as missing, extra, or replaced atoms, in crystalline materials with density functional theory (DFT). It covers charged defects and calculations of their stability and energy levels.

In plain words
What is it for?
It is for constructing vacancies, interstitials, substitutions, antisites, and defect complexes, then calculating formation energies, charge transition levels, concentrations, and carrier compensation.
Why use it?
It helps account for charge, chemical conditions, finite simulation size, and other effects that can make defect results misleading.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for constructing vacancies, interstitials, substitutions, antisites, and defect complexes, then calculating formation energies, charge transition levels, concentrations, and carrier compensation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sfetni/deep-matter-chem-skills/dft-defects
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 SFETNI/Deep-Matter-Chem-Skills --skill dft-defects
Clone the repo
git clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-Skills

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.

agentmods badge for dft-defects

README.md
[![agentmods](https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/dft-defects/github.svg)](https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/dft-defects)
Your own site
<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/dft-defects"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/dft-defects/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for dft-defects

Your own site · 80×15
<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/dft-defects"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/dft-defects.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 4 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 13,457 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00004 $0.13457
Opus 5 $0.00002 $0.06728
Sonnet 5 $0.00001 $0.02691
Haiku 4.5 $0.00000 $0.01346

Measured 11d ago against content hash 64d4a48c4217, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

dft-defects 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 11d ago.

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.

skills/electronic-structure/dft-defects/SKILL.md · 848 lines

How it starts

The opening of the file, as written. The whole thing — 848 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DFT Point Defect Calculations

Description

This skill covers the first-principles modeling of point defects in crystalline materials: vacancy, interstitial, antisite, substitutional, and complex defect construction; charge-state calculations; defect formation energy formalism with chemical potential constraints; finite-size corrections (Freysoldt-Neugebauer-Van de Walle, Kumagai-Oba, Makov-Payne); Fermi-level-dependent thermodynamics; transition levels; band-edge alignment; and defect concentration estimates. It uses VASP as the primary DFT backend with pymatgen (including the doped and shakenbreak packages) for automated defect generation, correction calculation, and thermodynamic analysis. Invoke this skill when computing defect formation energies, charge transition levels, defect concentrations, or carrier compensation in semiconductors and insulators.

Domain Context

Point defects are local disruptions of the perfect crystal lattice that control a wide range of functional properties: carrier concentration in semiconductors, ionic conductivity in solid electrolytes, color centers in optical materials, magnetic impurities, and catalyst poison tolerance. Even at thermodynamic equilibrium, defect concentrations of 10¹⁰–10¹⁸ cm⁻³ are common and technologically significant.

The fundamental challenge of DFT defect modeling is that defects break translational symmetry. A defect must be embedded in a finite supercell large enough that the defect's strain and electrostatic fields decay before the cell boundary. For charged defects, the periodic boundary conditions introduce spurious long-range interactions between charge images that must be corrected analytically. For semiconductors with DFT band-gap underestimation, formation energies of charged defects are biased by the incorrect VBM and CBM positions.

Defect formation energy is the central quantity:

E_f[X^q] = E_tot[X^q] - E_tot[bulk] - Σ_i n_i μ_i + q(E_VBM + E_F) + E_corr[q]

where:

  • E_tot[X^q] is the DFT total energy of the defect supercell in charge state q
  • E_tot[bulk] is the DFT total energy of the perfect bulk supercell (identical size)
  • n_i is the number of atoms of species i added (+) or removed (−) to form the defect
  • μ_i is the chemical potential of species i (eV; relative to its DFT elemental reference)
  • q is the formal charge state (integer; q > 0 = electron removed)
  • E_VBM is the DFT valence-band maximum energy of the bulk (in the same potential reference frame)
  • E_F is the Fermi level measured from the VBM (0 ≤ E_F ≤ E_gap)
  • E_corr[q] is the finite-size correction for charged defects (zero for q = 0)

Read the full file on GitHub · 848 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. 11d ago First seen · 848 lines · 4 tokens per session scan A 64d4a48c4217

Subscribe to this mod's changes

dft-defects is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 4 tokens to every session and 13,457 once invoked, about $0.0000 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 2026-08-31.

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