eos-fitting

eos-fitting is a skill for Claude Code, Codex from SFETNI/Deep-Matter-Chem-Skills. It costs 3 tokens per session (16,597 once invoked), scanned A, original, MIT.

A workflow for fitting equations of state (EOS), mathematical models that describe how a crystal's energy changes as its volume changes. It works with data from density functional theory (DFT) or machine-learning potentials and estimates equilibrium and bulk mechanical properties.

In plain words
What is it for?
Use it to create or analyze crystal energy-volume datasets, fit Birch-Murnaghan, Vinet, or Murnaghan models, estimate volume, energy, bulk modulus, and its pressure derivative, measure uncertainty, or compare DFT with a machine-learning potential.
Why use it?
It turns energy-versus-volume calculations into material properties and checks how reliable the fitted curve is. It also helps identify outliers or data affected by a phase transition.

Skill for Claude CodeCodex

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

Good fit Use it to create or analyze crystal energy-volume datasets, fit Birch-Murnaghan, Vinet, or Murnaghan models, estimate volume, energy, bulk modulus, and its pressure derivative, measure uncertainty, or compare DFT with a machine-learning potential.

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Install with agentmods
npx agentmods add skills/sfetni/deep-matter-chem-skills/eos-fitting
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 eos-fitting
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

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agentmods badge for eos-fitting

README.md
[![agentmods](https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/eos-fitting/github.svg)](https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/eos-fitting)
Your own site
<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/eos-fitting"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/eos-fitting/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 eos-fitting

Your own site · 80×15
<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/eos-fitting"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/eos-fitting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 3 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 16,597 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 $0.00003 $0.16597
Opus 5 $0.00002 $0.08299
Sonnet 5 $0.00001 $0.03319
Haiku 4.5 $0.00000 $0.01660

Measured 11d ago against content hash f3e7f0d2e762, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

eos-fitting 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/eos-fitting/SKILL.md · 1,082 lines

How it starts

The opening of the file, as written. The whole thing — 1,082 lines — stays where its author put it; the contents beside it link to each section on GitHub.

EOS Fitting

Description

This skill covers equation-of-state (EOS) fitting for crystalline materials from DFT or ML-potential calculations: generating energy-volume datasets via fixed-volume DFT relaxations, fitting Birch-Murnaghan, Vinet, and Murnaghan EOS forms, extracting equilibrium volume V₀, ground-state energy E₀, bulk modulus B₀, and pressure derivative B'₀, quantifying fitting uncertainty via bootstrap resampling, detecting outliers and phase-transition contamination, and comparing DFT versus MLP EOS for potential validation. Invoke this skill when extracting mechanical properties of crystalline materials, validating an ML potential against DFT ground-state properties, or generating reference EOS data for a materials database.

Domain Context

The equation of state relates the total energy (or pressure) of a crystal to its volume at fixed composition and temperature. For a DFT EOS at 0 K, the energy-volume curve E(V) is the Born-Oppenheimer ground-state total energy as a function of isotropic compression and dilation of the unit cell, with atomic positions and cell shape (but not volume) optimized at each volume. The EOS encodes the two most important bulk mechanical properties: the equilibrium volume V₀ and the isothermal bulk modulus B₀ = −V (∂P/∂V)_T = V (∂²E/∂V²)_T.

Several physical and numerical constraints govern EOS accuracy:

  • Pulay stress. Plane-wave DFT has an incomplete basis set at finite ENCUT. When the cell volume changes, the number and character of plane waves changes discontinuously, introducing a systematic error in the Hellmann-Feynman stress tensor. For a fixed ENCUT, the computed stress at a strained volume is systematically too small (tensile) relative to the true DFT stress. This shifts the apparent equilibrium volume. Pulay stress is reduced by increasing ENCUT to ≥ 1.3 × ENMAX (VASP) or verifying that residual stress from a static single-point is < 1 kbar after relaxation at the converged ENCUT.
  • Isotropic vs. anisotropic strain. The simplest EOS applies to isotropic (hydrostatic) volume change: all lattice vectors scaled uniformly by a factor η = (V/V₀)^{1/3}. For anisotropic crystals (tetragonal, orthorhombic, monoclinic, triclinic), isotropic scaling distorts the equilibrium shape. A more accurate protocol scales the volume while simultaneously optimizing the cell shape at each volume (ISIF=4 in VASP). The resulting EOS characterizes the ground-state energy as a function of volume at the optimal shape for each volume, not at a fixed metric tensor.
  • ISIF settings for fixed-volume relaxations. ISIF controls which degrees of freedom are relaxed in VASP: ISIF=2 (ions only, fixed cell), ISIF=4 (ions + cell shape, fixed volume), ISIF=3 (ions + cell shape + volume, full optimization). For EOS generation, ISIF=4 is the correct setting: it relaxes the internal structure and cell shape but keeps the volume fixed. Using ISIF=2 introduces shape-stress artifacts for non-cubic crystals. Using ISIF=3 would allow the volume to relax to equilibrium rather than to the target value.
  • Volume range and density. A typical EOS fit uses 7–15 volume points spanning ±5–15% of the equilibrium volume. Too narrow a range (±3%) underdetermines the curvature and gives an unreliable B'₀. Too wide a range (> ±20%) risks entering a different structural phase or a high-pressure regime where the 3rd-order Birch-Murnaghan EOS is invalid. For soft materials (polymorphs, molecular crystals, or materials with shallow E-V curves), even ±10% may cross a transition.
  • Magnetic state consistency. For magnetic materials (Fe, Ni, Co, Mn oxides, rare-earth intermetallics), the magnetic ground state may change with volume. A ferromagnetic ground state at equilibrium may become antiferromagnetic or paramagnetic at large compressions. If this transition occurs within the fitting range, the E-V curve has a kink and no single EOS form fits the entire range. The spin state must be monitored at each volume and the fit range restricted to a single-phase, single-spin-state region.

Read the full file on GitHub · 1,082 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 · 1,082 lines · 3 tokens per session scan A f3e7f0d2e762

Subscribe to this mod's changes

eos-fitting is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 3 tokens to every session and 16,597 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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