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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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Preis unbestätigt★ 161 GitHub-StarsVerzeichnis aktualisiert · 6. Sept. 2026agent-skill

Übersicht

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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Dateimetadaten

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

Originaltext anzeigen

---
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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Lizenz: MIT
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Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: 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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1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
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3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: learningmatter-mit/AtomisticSkills
Lizenz: MIT
Version: 1.0.0
Letzter GitHub-Push: 3. Sept. 2026
Verzeichnis aktualisiert: 6. Sept. 2026
Anleitungspfad: .agents/skills/chem-sorption-gcmc/SKILL.md @ d1f7ecfda2cd

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

66/100

Vielversprechend

Vertrauen

57/100

Do not auto-install

Audit

73/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

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

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

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Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "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"
  }
}

Für Ersteller

Quelle des Eintrags

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Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: learningmatter-mit
Quelle: learningmatter-mit/AtomisticSkills
Indexiert von: OpenAgentSkill Community-Index

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chem-sorption-gcmc

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