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chem-sorption-relax

Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools.

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

Übersicht

Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools.

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chem-sorption-relax

Goal

To process porous frameworks (e.g., MOFs, COFs) for downstream molecular sorption calculations. It checks if the unit cell's interplanar distances are large enough (usually ≥ 12 Å for typical gases) to avoid self-interaction of gas molecules across periodic boundaries. If not, it builds an appropriate supercell. Finally, it uses a standard Machine Learning Interatomic Potential (MLIP) workflow to relax the structure.

Prerequisites

Input: A framework structure in CIF (or XYZ) format.
MLIP MCP Tool: A relaxation tool such as mcp_fairchem_relax_structure, mcp_mace_relax_structure, or mcp_matgl_relax_structure.
Conda environment: base-agent for the supercell builder logic, followed by the specific environment for the chosen MLIP (e.g., fairchem-agent).

Instructions

Build Supercell (if necessary): Determine if the input framework needs to be expanded. Use the provided utility to read the input CIF, check interplanar distances, build a supercell if they are below the threshold, and save the result.

# Env: base-agent
python .agents/skills/chem-sorption-relax/scripts/build_supercell.py \
    --structure path/to/framework.cif \
    --min-plane-dist 12.0 \
    --output-cif ./out/framework_supercell.cif

[!TIP] If the script output indicates a 1x1x1 supercell was created (i.e. no expansion needed), you can just use your original CIF or the output CIF, as they will be identical.

Relax the Framework: Relax the output structure using the MCP server environment. Ensure that the correct MLIP is loaded first.

# Env: fairchem-agent (via MCP server)
mcp_fairchem_load_model(
    model_name="uma-s-1p2",
    device="auto"
)

mcp_fairchem_relax_structure(
    structure_data="./out/framework_supercell.cif",
    fmax=0.05,
    steps=500,
    optimizer="LBFGS",
    relax_cell=True,
    output_dir="./out/relaxed_framework"
)

relax_structure.py Parameters

--structure: Path to input CIF or XYZ.
--name: Identifier used in output filenames.
--calculator: Backend MLIP (fairchem, mace, matgl).
--model-name: Named model (e.g. uma-s-1p2) or full path to checkpoint.
--task-name: Multi-task head (omol, omat, odac, oc20, omc).
--optimizer: LBFGS (default) or FIRE.
--fmax: Force convergence threshold in eV/Å (default: 0.05).
--steps: Maximum optimizer steps (default: 500).
--relax-cell: Relax unit cell (default: True). Use --fixed-cell to fix cell.
--output-dir: Directory to save <name>.relaxed.cif and relax_results.json.

Proceed to downstream tasks: The relaxed CIF file (e.g. ./out/relaxed_framework/<name>.relaxed.cif) from step 2 is now ready for use in chem-sorption-widom and chem-sorption-gcmc.

Examples

Full workflow:

Build supercell:

# Env: base-agent
python .agents/skills/chem-sorption-relax/scripts/build_supercell.py \
    --structure my_cof.cif \
    --min-plane-dist 12.0 \
    --output-cif ./results/COF-1_supercell.cif

Relax with UMA-S-1p2 via MCP Tool:

mcp_fairchem_load_model(
    model_name="uma-s-1p2",
    device="auto"
)

mcp_fairchem_relax_structure(
    structure_data="./results/COF-1_supercell.cif",
    fmax=0.05,
    steps=500,
    optimizer="LBFGS",
    output_dir="./results/relaxed"
)

Constraints

Input Structure: The initial framework should be somewhat reasonable; highly distorted structures might fail during relaxation.
Minimum Distance: The --min-plane-dist should be at least 2 × (cut-off radius) of the probe gas interaction length (typically 12 Å for CO2 or N2).

Authors: Artur Lyssenko, Sauradeep Majumdar Contact: GitHub @arturlyssenko12, GitHub @sauradeep93

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Dateimetadaten

name: chem-sorption-relax
description: Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools.
category: [materials, chemistry]

Originaltext anzeigen

---
name: chem-sorption-relax
description: Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools.
category: [materials, chemistry]
---

# chem-sorption-relax

## Goal

To process porous frameworks (e.g., MOFs, COFs) for downstream molecular sorption calculations. It checks if the unit cell's interplanar distances are large enough (usually ≥ 12 Å for typical gases) to avoid self-interaction of gas molecules across periodic boundaries. If not, it builds an appropriate supercell. Finally, it uses a standard Machine Learning Interatomic Potential (MLIP) workflow to relax the structure.

## Prerequisites

- **Input**: A framework structure in CIF (or XYZ) format.
- **MLIP MCP Tool**: A relaxation tool such as `mcp_fairchem_relax_structure`, `mcp_mace_relax_structure`, or `mcp_matgl_relax_structure`.
- **Conda environment**: `base-agent` for the supercell builder logic, followed by the specific environment for the chosen MLIP (e.g., `fairchem-agent`).

## Instructions

1. **Build Supercell (if necessary)**: Determine if the input framework needs to be expanded. Use the provided utility to read the input CIF, check interplanar distances, build a supercell if they are below the threshold, and save the result.

```bash
# Env: base-agent
python .agents/skills/chem-sorption-relax/scripts/build_supercell.py \
    --structure path/to/framework.cif \
    --min-plane-dist 12.0 \
    --output-cif ./out/framework_supercell.cif
```

> [!TIP]
> If the script output indicates a `1x1x1` supercell was created (i.e. no expansion needed), you can just use your original CIF or the output CIF, as they will be identical.

2. **Relax the Framework**: Relax the output structure using the MCP server environment. Ensure that the correct MLIP is loaded first.

```python
# Env: fairchem-agent (via MCP server)
mcp_fairchem_load_model(
    model_name="uma-s-1p2",
    device="auto"
)

mcp_fairchem_relax_structure(
    structure_data="./out/framework_supercell.cif",
    fmax=0.05,
    steps=500,
    optimizer="LBFGS",
    relax_cell=True,
    output_dir="./out/relaxed_framework"
)
```

### relax_structure.py Parameters
- `--structure`: Path to input CIF or XYZ.
- `--name`: Identifier used in output filenames.
- `--calculator`: Backend MLIP (`fairchem`, `mace`, `matgl`).
- `--model-name`: Named model (e.g. `uma-s-1p2`) or full path to checkpoint.
- `--task-name`: Multi-task head (`omol`, `omat`, `odac`, `oc20`, `omc`).
- `--optimizer`: `LBFGS` (default) or `FIRE`.
- `--fmax`: Force convergence threshold in eV/Å (default: 0.05).
- `--steps`: Maximum optimizer steps (default: 500).
- `--relax-cell`: Relax unit cell (default: True). Use `--fixed-cell` to fix cell.
- `--output-dir`: Directory to save `<name>.relaxed.cif` and `relax_results.json`.

3. **Proceed to downstream tasks**:
The relaxed CIF file (e.g. `./out/relaxed_framework/<name>.relaxed.cif`) from step 2 is now ready for use in [chem-sorption-widom](../chem-sorption-widom/SKILL.md) and [chem-sorption-gcmc](../chem-sorption-gcmc/SKILL.md).

## Examples

**Full workflow:**

1. Build supercell:
```bash
# Env: base-agent
python .agents/skills/chem-sorption-relax/scripts/build_supercell.py \
    --structure my_cof.cif \
    --min-plane-dist 12.0 \
    --output-cif ./results/COF-1_supercell.cif
```

2. Relax with UMA-S-1p2 via MCP Tool:
```python
mcp_fairchem_load_model(
    model_name="uma-s-1p2",
    device="auto"
)

mcp_fairchem_relax_structure(
    structure_data="./results/COF-1_supercell.cif",
    fmax=0.05,
    steps=500,
    optimizer="LBFGS",
    output_dir="./results/relaxed"
)
```

## Constraints

- **Input Structure**: The initial framework should be somewhat reasonable; highly distorted structures might fail during relaxation.
- **Minimum Distance**: The `--min-plane-dist` should be at least 2 × (cut-off radius) of the probe gas interaction length (typically 12 Å for CO2 or N2).

---

**Authors:** Artur Lyssenko, Sauradeep Majumdar
**Contact:** [GitHub @arturlyssenko12](https://github.com/arturlyssenko12), [GitHub @sauradeep93](https://github.com/sauradeep93)

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Lizenz: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
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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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-relax/SKILL.md @ d1f7ecfda2cd

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

66/100

Vielversprechend

Vertrauen

65/100

Nur Sandbox

Audit

76/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
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: —

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Weitere Details

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  "endpoints": {
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    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-sorption-relax",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-sorption-relax/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-sorption-relax&task=Use%20chem-sorption-relax%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-sorption-relax%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-sorption-relax%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-sorption-relax/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-sorption-relax"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

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

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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Dieser Registry-indexiert-Eintrag wird learningmatter-mit zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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

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Übersicht

chem-sorption-relax

Goal

Prerequisites

Instructions

relax_structure.py Parameters

Examples

Constraints

Quelle prüfen

Preis und Betriebskosten

Mit einer kleinen Aufgabe beginnen

Quelle und Nutzungshinweise

Agent-Zugang

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Registry-indexiert

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Evidenz-Badges in deine README einfügen

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