chem-db-qmof: Reusable for AI Agents | OpenAgentSkill

Overview

Query the Quantum MOF (QMOF) database via Materials Project's MPContribs platform for DFT-computed properties (bandgap) and optimized crystal structures of Metal-Organic Frameworks.

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chem-db-qmof

Goal

To retrieve computational data and relaxed crystal structures (.cif) for roughly 20,000 Metal-Organic Frameworks (MOFs) that have been thoroughly characterized using DFT. QMOF is hosted by the Materials Project under the "MPContribs" project (qmof). This skill allows you to retrieve relaxed CIF structures by reference name, CSD refcode, or specific structural/electronic properties like bandgap.

Prerequisites

Dependency: mpcontribs-client must be installed.
Environment: Execution happens in the base-agent environment.
Credentials: Requires the standard Materials Project API key exported as MP_API_KEY.

Instructions

Query Database: Use the provided script query_qmof.py to search by formula or identifier.

# Env: base-agent
python .agents/skills/chem-db-qmof/scripts/query_qmof.py \
    --formula "Zn" \
    --max-results 5 \
    --output-dir ./research/qmof_results

Available Arguments:

--formula: Search by chemical formula or elements (e.g., Zn,O,C).
--identifier: Search by specific CSD refcode or MOF common name (e.g., KAXQIL).
--max-results: Maximum number of structures to download (default: 5).
--output-dir: Directory to save the resulting .cif files.

Examples

Example 1: Automated testing script (Zinc MOF)

# Env: base-agent
bash .agents/skills/chem-db-qmof/examples/test_qmof.sh

Example 2: Query for 5 MOFs containing Zinc manually

# Env: base-agent
python .agents/skills/chem-db-qmof/scripts/query_qmof.py \
    --formula "Zn" \
    --max-results 5 \
    --output-dir ./results/qmof_zn

Example 2: Retrieve a specific MOF by identifier

# Env: base-agent
python .agents/skills/chem-db-qmof/scripts/query_qmof.py \
    --identifier "KAXQIL" \
    --max-results 1 \
    --output-dir ./results/qmof_kaxqil

Constraints

API Limits: The Materials Project/MPContribs API has rate limits. Do not use extremely large values for --max-results unless necessary.
Availability: If you cannot find a MOF in QMOF via the script, or if the API query is overly complex, you can also search the QMOF database manually through the Materials Project web interface under the "Contributions" section.

Author: Bowen Deng Contact: GitHub @learningmatter-mit

View source ↗

File metadata

name: chem-db-qmof
description: Query the Quantum MOF (QMOF) database via Materials Project's MPContribs platform for DFT-computed properties (bandgap) and optimized crystal structures of Metal-Organic Frameworks.
category: [materials, chemistry]

View original text

---
name: chem-db-qmof
description: Query the Quantum MOF (QMOF) database via Materials Project's MPContribs platform for DFT-computed properties (bandgap) and optimized crystal structures of Metal-Organic Frameworks.
category: [materials, chemistry]
---

# chem-db-qmof

## Goal

To retrieve computational data and relaxed crystal structures (.cif) for roughly 20,000 Metal-Organic Frameworks (MOFs) that have been thoroughly characterized using DFT. QMOF is hosted by the Materials Project under the "MPContribs" project (`qmof`). This skill allows you to retrieve relaxed CIF structures by reference name, CSD refcode, or specific structural/electronic properties like bandgap.

## Prerequisites

- **Dependency**: `mpcontribs-client` must be installed.
- **Environment**: Execution happens in the `base-agent` environment.
- **Credentials**: Requires the standard Materials Project API key exported as `MP_API_KEY`.

## Instructions

1. **Query Database**: Use the provided script `query_qmof.py` to search by formula or identifier.

```bash
# Env: base-agent
python .agents/skills/chem-db-qmof/scripts/query_qmof.py \
    --formula "Zn" \
    --max-results 5 \
    --output-dir ./research/qmof_results
```

### Available Arguments:
- `--formula`: Search by chemical formula or elements (e.g., `Zn,O,C`).
- `--identifier`: Search by specific CSD refcode or MOF common name (e.g., `KAXQIL`).
- `--max-results`: Maximum number of structures to download (default: 5).
- `--output-dir`: Directory to save the resulting `.cif` files.

## Examples

**Example 1: Automated testing script (Zinc MOF)**
```bash
# Env: base-agent
bash .agents/skills/chem-db-qmof/examples/test_qmof.sh
```

**Example 2: Query for 5 MOFs containing Zinc manually**
```bash
# Env: base-agent
python .agents/skills/chem-db-qmof/scripts/query_qmof.py \
    --formula "Zn" \
    --max-results 5 \
    --output-dir ./results/qmof_zn
```

**Example 2: Retrieve a specific MOF by identifier**
```bash
# Env: base-agent
python .agents/skills/chem-db-qmof/scripts/query_qmof.py \
    --identifier "KAXQIL" \
    --max-results 1 \
    --output-dir ./results/qmof_kaxqil
```

## Constraints

- **API Limits**: The Materials Project/MPContribs API has rate limits. Do not use extremely large values for `--max-results` unless necessary.
- **Availability**: If you cannot find a MOF in QMOF via the script, or if the API query is overly complex, you can also search the QMOF database manually through the Materials Project web interface under the "Contributions" section.

---

**Author:** Bowen Deng
**Contact:** [GitHub @learningmatter-mit](https://github.com/learningmatter-mit)

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Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

Dependency or permission surface needs review
Permission surface may require sandboxing
The SKILL.md example code block for 'Example 1' appears to have a formatting issue (unclosed code fence) in the provided excerpt, though the actual file may be correct.
The script snippet in the documentation is truncated, but the full script is present in the repository.

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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-db-qmof/SKILL.md @ d1f7ecfda2cd

Version reported in registry metadata; check source releases before relying on it.

Quality

69/100

Promising

Trust

57/100

Do not auto-install

Audit

75/100

Needs review

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