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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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Harga belum dikonfirmasi★ 161 Star GitHubDirektori diperbarui · 6 Sep 2026agent-skill

Ringkasan

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

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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

Lihat sumber ↗

Metadata berkas

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

Lihat teks asli

---
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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Repositori sumber: learningmatter-mit/AtomisticSkills
Lisensi: MIT
Versi: 1.0.0
Push GitHub terakhir: 3 Sep 2026
Direktori diperbarui: 6 Sep 2026
Jalur instruksi: .agents/skills/chem-conformer-search/SKILL.md @ d1f7ecfda2cd

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

Kualitas

66/100

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65/100

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Audit

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
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
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  ],
  "agent_contract": {
    "task_input": "Use chem-conformer-search in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "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"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: learningmatter-mit
Sumber: learningmatter-mit/AtomisticSkills
Diindeks oleh: Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan learningmatter-mit, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

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

Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.