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chem-sorption-gcmc

Calculates gas adsorption isotherms via BVT/GCMC Monte Carlo simulations in a porous framework using MLIP.

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Prix non confirmé★ 161 Stars GitHubRegistre mis à jour · 6 sept. 2026agent-skill

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Calculates gas adsorption isotherms via BVT/GCMC Monte Carlo simulations in a porous framework using MLIP.

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chem-sorption-gcmc

Goal

To predict the macroscopic adsorption uptake of a gas (or gas mixture) in a porous material at a specific temperature and pressure. The skill relies on Grand Canonical Monte Carlo (GCMC) simulations where the host-guest and guest-guest interactions are calculated using a Machine Learning Interatomic Potential (MLIP: MACE, FairChem, 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

Perform Single-Component GCMC (Optional): If you are investigating a single gas species, use run_gcmc.py.

# Env: fairchem-agent (or other MLIP-specific env)
python .agents/skills/chem-sorption-gcmc/scripts/run_gcmc.py \
    --cif path/to/relaxed_supercell.cif \
    --calculator fairchem \
    --model-name uma-s-1p1 \
    --task-name omol \
    --steps 50000 \
    --temperature-K 298 \
    --pressure-bar 1.0 \
    --adsorbate CO2 \
    --output-dir ./results/single_gcmc

Perform Multi-Component GCMC (Optional): If you are simulating a gas mixture (e.g. flue gas separation 15% CO2 / 85% N2), use run_gcmc_multi.py.

# Env: fairchem-agent
python .agents/skills/chem-sorption-gcmc/scripts/run_gcmc_multi.py \
    --cif path/to/relaxed_supercell.cif \
    --calculator fairchem \
    --model-name uma-s-1p1 \
    --task-name omol \
    --steps 50000 \
    --temperature-K 298 \
    --gases CO2 N2 \
    --y 0.15 0.85 \
    --p-total-bar 1.0 \
    --output-dir ./results/multi_gcmc

Key Parameters

--cif: Path to the relaxed host framework.
--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, required by some models (omol for UMA and MACE-MH).
--steps: Number of Monte Carlo steps (minimum 50,000 recommended for equilibration).
--temperature-K: Sim temperature.
--pressure-bar (Single): Gas pressure in bar.
--p-total-bar (Multi): Total mixture pressure in bar.
--gases / --y (Multi): Species list and corresponding mole fractions in the vapor phase.

Examples

Example 1: Generating an Isotherm Point (CO2, 0.1 bar, 298K) with UMA:

# Env: fairchem-agent
python .agents/skills/chem-sorption-gcmc/scripts/run_gcmc.py \
    --cif ./data/MOF-5_supercell.cif \
    --calculator fairchem \
    --model-name uma-s-1p1 \
    --task-name omol \
    --steps 50000 \
    --temperature-K 298 \
    --pressure-bar 0.1 \
    --adsorbate CO2 \
    --output-dir ./out/0.1_bar

Constraints

Simulation Time: GCMC with MLIPs can be computationally intensive. Use GPUs when available (--device cuda).
Equilibration: You MUST check the generated nmols.png and energy.png inside the output-dir to visually confirm that the number of molecules and energy have plateaued (equilibrated). If the trend is still rising/falling at the end of the simulation, you must re-run with more --steps (or restart the trajectory).
Restarting: You can pass --restart-traj ./out/mc.traj to continue a previous run.

Author: Artur Lyssenko Contact: GitHub @arturlyssenko12

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Métadonnées du fichier

name: chem-sorption-gcmc
description: Calculates gas adsorption isotherms via BVT/GCMC Monte Carlo simulations in a porous framework using MLIP.
category: [materials, chemistry]

Voir le texte original

---
name: chem-sorption-gcmc
description: Calculates gas adsorption isotherms via BVT/GCMC Monte Carlo simulations in a porous framework using MLIP.
category: [materials, chemistry]
---

# chem-sorption-gcmc

## Goal

To predict the macroscopic adsorption uptake of a gas (or gas mixture) in a porous material at a specific temperature and pressure. The skill relies on Grand Canonical Monte Carlo (GCMC) simulations where the host-guest and guest-guest interactions are calculated using a Machine Learning Interatomic Potential (MLIP: MACE, FairChem, MatGL).

## Prerequisites

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

## Instructions

1. **Perform Single-Component GCMC (Optional)**: If you are investigating a single gas species, use `run_gcmc.py`.

```bash
# Env: fairchem-agent (or other MLIP-specific env)
python .agents/skills/chem-sorption-gcmc/scripts/run_gcmc.py \
    --cif path/to/relaxed_supercell.cif \
    --calculator fairchem \
    --model-name uma-s-1p1 \
    --task-name omol \
    --steps 50000 \
    --temperature-K 298 \
    --pressure-bar 1.0 \
    --adsorbate CO2 \
    --output-dir ./results/single_gcmc
```

2. **Perform Multi-Component GCMC (Optional)**: If you are simulating a gas mixture (e.g. flue gas separation 15% CO2 / 85% N2), use `run_gcmc_multi.py`.

```bash
# Env: fairchem-agent
python .agents/skills/chem-sorption-gcmc/scripts/run_gcmc_multi.py \
    --cif path/to/relaxed_supercell.cif \
    --calculator fairchem \
    --model-name uma-s-1p1 \
    --task-name omol \
    --steps 50000 \
    --temperature-K 298 \
    --gases CO2 N2 \
    --y 0.15 0.85 \
    --p-total-bar 1.0 \
    --output-dir ./results/multi_gcmc
```

### Key Parameters
- `--cif`: Path to the relaxed host framework.
- `--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, required by some models (`omol` for UMA and MACE-MH).
- `--steps`: Number of Monte Carlo steps (minimum 50,000 recommended for equilibration).
- `--temperature-K`: Sim temperature.
- `--pressure-bar` (Single): Gas pressure in bar.
- `--p-total-bar` (Multi): Total mixture pressure in bar.
- `--gases` / `--y` (Multi): Species list and corresponding mole fractions in the vapor phase.

## Examples

**Example 1: Generating an Isotherm Point (CO2, 0.1 bar, 298K) with UMA:**
```bash
# Env: fairchem-agent
python .agents/skills/chem-sorption-gcmc/scripts/run_gcmc.py \
    --cif ./data/MOF-5_supercell.cif \
    --calculator fairchem \
    --model-name uma-s-1p1 \
    --task-name omol \
    --steps 50000 \
    --temperature-K 298 \
    --pressure-bar 0.1 \
    --adsorbate CO2 \
    --output-dir ./out/0.1_bar
```

## Constraints
- **Simulation Time**: GCMC with MLIPs can be computationally intensive. Use GPUs when available (`--device cuda`).
- **Equilibration**: You MUST check the generated `nmols.png` and `energy.png` inside the `output-dir` to visually confirm that the number of molecules and energy have plateaued (equilibrated). If the trend is still rising/falling at the end of the simulation, you must re-run with more `--steps` (or restart the trajectory).
- **Restarting**: You can pass `--restart-traj ./out/mc.traj` to continue a previous run.

---

**Author:** Artur Lyssenko
**Contact:** [GitHub @arturlyssenko12](https://github.com/arturlyssenko12)

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Licence: MIT
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Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
The SKILL.md does not explain the output file formats (e.g., gcmc_results.json, multi_gcmc.json) or how to interpret the results (e.g., uptake units, selectivity).
The example uses only 100 Monte Carlo steps, which is far below the recommended minimum of 50,000 for real simulations; this could mislead users into thinking such short runs are sufficient.
The term 'BVT' is used without explanation; users unfamiliar with the ensemble may be confused.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
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Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: learningmatter-mit/AtomisticSkills
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 3 sept. 2026
Registre mis à jour: 6 sept. 2026
Chemin des instructions: .agents/skills/chem-sorption-gcmc/SKILL.md @ d1f7ecfda2cd

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

66/100

Prometteur

Confiance

57/100

Do not auto-install

Audit

73/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
The SKILL.md does not explain the output file formats (e.g., gcmc_results.json, multi_gcmc.json) or how to interpret the results (e.g., uptake units, selectivity).
The example uses only 100 Monte Carlo steps, which is far below the recommended minimum of 50,000 for real simulations; this could mislead users into thinking such short runs are sufficient.
The term 'BVT' is used without explanation; users unfamiliar with the ensemble may be confused.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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      "skill_slug": "learningmatter-mit-chem-sorption-gcmc",
      "task": "Use chem-sorption-gcmc in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/learningmatter-mit-chem-sorption-gcmc",
    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-sorption-gcmc",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-sorption-gcmc/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-sorption-gcmc&task=Use%20chem-sorption-gcmc%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-sorption-gcmc%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-sorption-gcmc%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-sorption-gcmc/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-sorption-gcmc"
  }
}

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Créateur: learningmatter-mit
Source: learningmatter-mit/AtomisticSkills
Indexé par: Index communautaire OpenAgentSkill

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chem-sorption-gcmc

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