chem-sorption-gcmc: Reusable for AI Agents | OpenAgentSkill

Overview

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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File metadata

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

View original text

---
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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Review before install: Avoid automatic install

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.

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License: MIT
Version: 1.0.0
Last GitHub push: Sep 3, 2026
Registry updated: Sep 6, 2026
Instruction path: .agents/skills/chem-sorption-gcmc/SKILL.md @ d1f7ecfda2cd

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69/100

Promising

Trust

58/100

Do not auto-install

Audit

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Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-sorption-gcmc?metric=listed&label=Listed)](https://www.openagentskill.com/skills/learningmatter-mit-chem-sorption-gcmc?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-sorption-gcmc?metric=trust&label=Trust)](https://www.openagentskill.com/skills/learningmatter-mit-chem-sorption-gcmc?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-sorption-gcmc?metric=audit&label=Audit)](https://www.openagentskill.com/skills/learningmatter-mit-chem-sorption-gcmc/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-sorption-gcmc?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/learningmatter-mit-chem-sorption-gcmc?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

Preview badge Open audit Creator kit

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.