chem-sorption-widom

chem-sorption-widom is a skill for Claude Code, Codex from learningmatter-mit/AtomisticSkills. It costs 32 tokens per session (909 once invoked), scanned A, original, MIT.

A scientific calculation of how strongly a gas initially interacts with a porous material, such as a metal-organic framework or covalent-organic framework. It uses Widom insertion and a machine-learning interatomic potential to estimate the Henry coefficient and adsorption heat.

In plain words
What is it for?
Use it with a relaxed CIF or XYZ structure to calculate initial affinity for a gas such as CO₂, including the Henry coefficient and isosteric heat of adsorption.
Why use it?
It provides estimates of gas uptake affinity without requiring a full adsorption simulation at many gas concentrations. The workflow also accounts for the chosen calculation model and temperature.

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/chem-sorption-widom
Any agent
npx skills add learningmatter-mit/AtomisticSkills --skill chem-sorption-widom
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.

agentmods badge for chem-sorption-widom

README.md
[![agentmods](https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/chem-sorption-widom.svg)](https://agentmods.dev/skills/learningmatter-mit/atomisticskills/chem-sorption-widom)
Your own site
<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/chem-sorption-widom"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/chem-sorption-widom.svg" alt="Measured on agentmods" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 909 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 $0.00032 $0.00909
Opus 5 $0.00016 $0.00454
Sonnet 5 $0.00006 $0.00182
Haiku 4.5 $0.00003 $0.00091

Measured 5d ago against content hash c2bbddf3802f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

chem-sorption-widom 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 5d ago.

The scan reads SKILL.md. This mod also ships 9 executable files (examples/test_widom.sh, scripts/run_widom.py, scripts/widom_common.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/chem-sorption-widom/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

chem-sorption-widom

Goal

To determine the initial affinity of a porous material (e.g., MOFs, COFs) for a specific gas molecule at infinite dilution. This is done by computing the Henry coefficient ($K_H$) and the isosteric heat of adsorption ($\Delta H_{ads}$) using Widom insertion, calculating interaction energies with a generic Machine Learning Interatomic Potential (MLIP) such as MACE, FairChem, or MatGL.

Prerequisites

  • Input: A relaxed framework structure in CIF (or XYZ) format. The structure should ideally be processed by chem-sorption-relax to ensure proper supercell dimensions.
  • Conda environment: Depends on the MLIP used (e.g., fairchem-agent, mace-agent, matgl-agent).

Instructions

  1. Perform Widom Insertion: Use the run_widom.py script, specifying the structure, gas, temperature, and your MLIP of choice.
# Env: fairchem-agent (if using fairchem), mace-agent (if using mace), etc.
python .agents/skills/chem-sorption-widom/scripts/run_widom.py \
    --structure path/to/relaxed_supercell.cif \
    --name MY_FRAMEWORK \
    --calculator fairchem \
    --model-name uma-s-1p2 \
    --task-name omol \
    --gas CO2 \
    --temperature 298 \
    --output-dir ./results

Parameters

  • --structure: Path to the relaxed host framework (must be large enough, see Constraints).
  • --name: Identifier for the output files.
  • --calculator: The backend MLIP (fairchem, mace, matgl).
  • --model-name: Name or path to the MLIP weights (e.g., uma-s-1p1.pt, MACE-MH-1).
  • --task-name: Optional, but highly recommended for multi-task models (e.g., omol for FairChem UMA and MACE-MH).
  • --gas: The adsorbate gas (e.g., CO2, N2, CH4).
  • --temperature: Temperature in Kelvin.
  • --num-insertions: Number of Monte Carlo insertion attempts (default: 50,000).
  • --output-dir: Directory to save the widom_results.json.

Examples

Example 1: Using FairChem UMA-S-1p2 for CO2 adsorption at 298K

# Env: fairchem-agent
python .agents/skills/chem-sorption-widom/scripts/run_widom.py \
    --structure ./results/COF-1_supercell.cif \
    --name COF-1 \
    --calculator fairchem \
    --model-name uma-s-1p2 \
    --task-name omol \
    --gas CO2 \
    --temperature 298 \
    --output-dir ./results

Read the full file on GitHub · 76 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. 5d ago First seen · 76 lines · 32 tokens per session scan A c2bbddf3802f

Subscribe to this mod's changes

chem-sorption-widom is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 909 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 2026-08-30.

Related

Other skills, from other repositories

datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…

synthetic-sciences/openscience · 67 tokens

deepchem

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first…

synthetic-sciences/openscience · 78 tokens

hypogenic

Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use…

synthetic-sciences/openscience · 69 tokens

binding-affinity

Hybrid ML + physics binding affinity prediction. Empirical scoring, MM/GBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.

synthetic-sciences/openscience · 38 tokens

drug-design

End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.

synthetic-sciences/openscience · 44 tokens

medchem

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

synthetic-sciences/openscience · 39 tokens