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chem-conformer-search

Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.

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Precio sin confirmar★ 161 Estrellas de GitHubRegistro actualizado · 6 sept 2026agent-skill

Resumen

Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.

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Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

Molecular Conformer Search & Ranking

Goal

Generate a diverse ensemble of low-energy conformers for a given molecule. The workflow combines:

Stochastic sampling using RDKit's ETKDG algorithm (Experimental Torsion Distance Geometry).
High-accuracy relaxation using Machine Learning Interatomic Potentials (MLIPs) to get near-DFT quality geometries and energies.
Deduplication and Boltzmann weighting to identify the most relevant conformers at finite temperature.

[!IMPORTANT] This skill is optimized for organic molecules and uses MACE-OFF23 models by default. For inorganic clusters, switch to MACE-OMAT or MatGL models.

Recommended Models

MACE-OFF23: MACE-OFF23-small (default), MACE-OFF23-medium — trained on organic molecules (Env: mace-agent)
MACE-MH: MACE-MH-1 with head omol — multi-head model with molecular head (Env: mace-agent)
UMA: uma-s-1p1 with head omol — general molecular model (Env: fairchem-agent)

1. Prerequisites

Conda Environment: mace-agent (recommended as it includes both mace and rdkit).
Input: SMILES string or a structure file (.xyz, .sdf, .mol2, .pdb).

2. Methodology

Generation: Generate N initial conformers using RDKit's EmbedMultipleConfs with ETKDGv3.
Relaxation: Optimize the geometry of each conformer using the selected MLIP (fmax = 0.01 eV/Å).
Deduplication/Clustering: Filter redundant conformers by simple RMSD thresholding (default), Hierarchical clustering, or K-Means clustering. Only the lowest-energy conformer in each cluster is kept.
Ranking: Sort unique conformers by energy.
Boltzmann Weighting: Calculate population probability $P_i$ at temperature $T$: $$P_i = \frac{e^{-(E_i - E_{min}) / k_B T}}{\sum_j e^{-(E_j - E_{min}) / k_B T}}$$

3. Usage

Basic Usage (SMILES)

Generate 30 conformers for a molecule (e.g., aspirin) and relax with MACE-OFF23:

# Env: mace-agent
python .agents/skills/chem-conformer-search/scripts/conformer_search.py \
    --smiles "CC(=O)Oc1ccccc1C(=O)O" \
    --num_conformers 30 \
    --output_dir research/aspirin_conformers

Advanced Usage (Structure File + Options)

# Env: mace-agent
python .agents/skills/chem-conformer-search/scripts/conformer_search.py \
    --structure my_molecule.sdf \
    --num_conformers 100 \
    --rms_threshold 0.5 \
    --dedup_threshold 0.1 \
    --temperature 298.15 \
    --model_type mace \
    --model_name MACE-OFF23-small \
    --device cuda \
    --output_dir research/my_molecule_search

Key Parameters

Argument	Default	Description
`--smiles`	-	SMILES string of the molecule
`--structure`	-	Path to input structure file (alternative to SMILES)
`--num_conformers`	50	Number of initial conformers to generate with RDKit
`--rms_threshold`	0.2	RDKit pruning threshold (Å) to discard similar initial conformers
`--clustering`	`rmsd`	Method to filter conformers: `rmsd`, `hierarchical`, or `kmeans`
`--dedup_threshold`	0.1	Post-relaxation RMSD threshold (Å) to merge identical conformers or cut for hierarchical
`--num_clusters`	5	Number of clusters if `--clustering kmeans` is used
`--energy_threshold`	0.5	Max energy above global minimum (eV) to keep before RMSD comparison. Set to 0 to disable
`--fmax`	0.01	Force convergence criterion for relaxation (eV/Å)
`--temperature`	298.15	Temperature (K) for Boltzmann weighting
`--model_type`	`mace`	MLIP backend (`mace`, `matgl`, `fairchem`)
`--model_name`	`MACE-OFF23-small`	Specific model checkpoint to use

4. Output Files

The output directory will contain:

conformer_results.json: A summary file containing:
- List of unique conformers with their energies, relative energies, and Boltzmann weights.
- Paths to the corresponding XYZ files.
- Metadata (model used, parameters).
conf_000.xyz, conf_001.xyz, ...: The relaxed structures of the unique conformers, sorted by energy (000 is the global minimum found).

5. Examples

See examples/aspirin for a complete example run on Acetylsalicylic acid.

6. Constraints

Environment: Use the conda environment matching the chosen model: mace-agent (MACE), fairchem-agent (FairChem/UMA), or matgl-agent (MatGL). All include RDKit.
Input: Either --smiles or --structure must be provided, but not both.
Molecule Type: Optimized for organic molecules. For inorganic clusters, switch to MACE-OMAT or MatGL models.
Non-periodic: All conformers are treated as non-periodic (isolated molecules).

7. References

Riniker, S.; Landrum, G. A., "Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation", J. Chem. Inf. Model., 2015, 55, 2562. DOI

Author: Bowen Deng Contact: GitHub @learningmatter-mit

Ver fuente ↗

Metadatos del archivo

name: chem-conformer-search
description: Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
category: [chemistry]

Ver texto original

---
name: chem-conformer-search
description: Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
category: [chemistry]
---

# Molecular Conformer Search & Ranking

## Goal

Generate a diverse ensemble of low-energy conformers for a given molecule. The workflow combines:
1.  **Stochastic sampling** using RDKit's ETKDG algorithm (Experimental Torsion Distance Geometry).
2.  **High-accuracy relaxation** using Machine Learning Interatomic Potentials (MLIPs) to get near-DFT quality geometries and energies.
3.  **Deduplication** and **Boltzmann weighting** to identify the most relevant conformers at finite temperature.

> [!IMPORTANT]
> This skill is optimized for **organic molecules** and uses `MACE-OFF23` models by default. For inorganic clusters, switch to `MACE-OMAT` or `MatGL` models.

### Recommended Models

- **MACE-OFF23**: `MACE-OFF23-small` (default), `MACE-OFF23-medium` — trained on organic molecules (Env: `mace-agent`)
- **MACE-MH**: `MACE-MH-1` with head `omol` — multi-head model with molecular head (Env: `mace-agent`)
- **UMA**: `uma-s-1p1` with head `omol` — general molecular model (Env: `fairchem-agent`)

## 1. Prerequisites

- **Conda Environment**: `mace-agent` (recommended as it includes both `mace` and `rdkit`).
- **Input**: SMILES string or a structure file (`.xyz`, `.sdf`, `.mol2`, `.pdb`).

## 2. Methodology

1.  **Generation**: Generate `N` initial conformers using RDKit's `EmbedMultipleConfs` with ETKDGv3.
2.  **Relaxation**: Optimize the geometry of *each* conformer using the selected MLIP (fmax = 0.01 eV/Å).
3.  **Deduplication/Clustering**: Filter redundant conformers by simple RMSD thresholding (default), Hierarchical clustering, or K-Means clustering. Only the lowest-energy conformer in each cluster is kept.
4.  **Ranking**: Sort unique conformers by energy.
5.  **Boltzmann Weighting**: Calculate population probability $P_i$ at temperature $T$:
    $$P_i = \frac{e^{-(E_i - E_{min}) / k_B T}}{\sum_j e^{-(E_j - E_{min}) / k_B T}}$$

## 3. Usage

### Basic Usage (SMILES)

Generate 30 conformers for a molecule (e.g., aspirin) and relax with MACE-OFF23:

```bash
# Env: mace-agent
python .agents/skills/chem-conformer-search/scripts/conformer_search.py \
    --smiles "CC(=O)Oc1ccccc1C(=O)O" \
    --num_conformers 30 \
    --output_dir research/aspirin_conformers
```

### Advanced Usage (Structure File + Options)

```bash
# Env: mace-agent
python .agents/skills/chem-conformer-search/scripts/conformer_search.py \
    --structure my_molecule.sdf \
    --num_conformers 100 \
    --rms_threshold 0.5 \
    --dedup_threshold 0.1 \
    --temperature 298.15 \
    --model_type mace \
    --model_name MACE-OFF23-small \
    --device cuda \
    --output_dir research/my_molecule_search
```

### Key Parameters

| Argument | Default | Description |
|:---|:---|:---|
| `--smiles` | - | SMILES string of the molecule |
| `--structure` | - | Path to input structure file (alternative to SMILES) |
| `--num_conformers` | 50 | Number of initial conformers to generate with RDKit |
| `--rms_threshold` | 0.2 | RDKit pruning threshold (Å) to discard similar initial conformers |
| `--clustering` | `rmsd` | Method to filter conformers: `rmsd`, `hierarchical`, or `kmeans` |
| `--dedup_threshold` | 0.1 | Post-relaxation RMSD threshold (Å) to merge identical conformers or cut for hierarchical |
| `--num_clusters` | 5 | Number of clusters if `--clustering kmeans` is used |
| `--energy_threshold` | 0.5 | Max energy above global minimum (eV) to keep before RMSD comparison. Set to 0 to disable |
| `--fmax` | 0.01 | Force convergence criterion for relaxation (eV/Å) |
| `--temperature` | 298.15 | Temperature (K) for Boltzmann weighting |
| `--model_type` | `mace` | MLIP backend (`mace`, `matgl`, `fairchem`) |
| `--model_name` | `MACE-OFF23-small` | Specific model checkpoint to use |

## 4. Output Files

The output directory will contain:

1.  **`conformer_results.json`**: A summary file containing:
    -   List of unique conformers with their energies, relative energies, and Boltzmann weights.
    -   Paths to the corresponding XYZ files.
    -   Metadata (model used, parameters).
2.  **`conf_000.xyz`, `conf_001.xyz`, ...**: The relaxed structures of the unique conformers, sorted by energy (000 is the global minimum found).

## 5. Examples

See `examples/aspirin` for a complete example run on Acetylsalicylic acid.

## 6. Constraints

- **Environment**: Use the conda environment matching the chosen model: `mace-agent` (MACE), `fairchem-agent` (FairChem/UMA), or `matgl-agent` (MatGL). All include RDKit.
- **Input**: Either `--smiles` or `--structure` must be provided, but not both.
- **Molecule Type**: Optimized for organic molecules. For inorganic clusters, switch to `MACE-OMAT` or `MatGL` models.
- **Non-periodic**: All conformers are treated as non-periodic (isolated molecules).

## 7. References

- Riniker, S.; Landrum, G. A., "Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation", *J. Chem. Inf. Model.*, 2015, 55, 2562. [DOI](https://doi.org/10.1021/acs.jcim.5b00654)

---

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

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Repositorio fuente: learningmatter-mit/AtomisticSkills
Licencia: MIT
Versión: 1.0.0
Último push de GitHub: 3 sept 2026
Registro actualizado: 6 sept 2026
Ruta de instrucciones: .agents/skills/chem-conformer-search/SKILL.md @ d1f7ecfda2cd

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

66/100

Prometedor

Confianza

65/100

Solo sandbox

Auditoría

76/100

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Dependency or permission surface needs review
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Financial research output is not financial advice; require human review before any live investment decision
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Quality score needs review
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Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata
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    "minimum_review_before_use": [
      "Trust: 73/100 Strong shortlist",
      "Audit: 76/100 Needs review",
      "Safety: 36/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "learningmatter-mit-chem-conformer-search (chem-conformer-search)",
      "install_command": "npx skills add learningmatter-mit/AtomisticSkills --skill chem-conformer-search",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "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-conformer-search",
      "task": "Use chem-conformer-search 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-conformer-search",
    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-conformer-search",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-conformer-search/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-conformer-search&task=Use%20chem-conformer-search%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-conformer-search%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-conformer-search%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-conformer-search/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-conformer-search"
  }
}

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Creador: learningmatter-mit
Fuente: learningmatter-mit/AtomisticSkills
Indexado por: Índice comunitario de OpenAgentSkill

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