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chem-react-ot

Generate transition state structures for chemical reactions using React-OT.

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Prix non confirmé★ 161 Stars GitHubRegistre mis à jour · 6 sept. 2026agent-skill

Vue d’ensemble

Generate transition state structures for chemical reactions using React-OT.

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`chem-react-ot` — React-OT Transition State Generation

Goal

Generate transition state (TS) structures given reactant and product structures using the React-OT model (Optimal Transport). React-OT is a generative model that predicts TS geometries directly without requiring an initial guess path (like NEB).

Category: chemistry Environment: react-ot-agent

Key Features

Generative TS Prediction: Predicts 3D transition state structures from 3D reactants and products.
Fast Inference: Uses an ODE solver for generation, typically much faster than DFT-based NEB.
No Path Guess Required: Directly generates the TS structure.

Usage

1. Environment Setup

This skill requires the react-ot-agent conda environment. Ensure it is installed:

# Env: react-ot-agent
cd conda-envs/react-ot-agent
bash install.sh

2. Download Models

Before running the skill for the first time, download the pre-trained model weights:

# activate react-ot-agent first
conda activate react-ot-agent
python conda-envs/react-ot-agent/download_models.py

The checkpoint is saved to ~/.cache/react-ot/checkpoints/sb-pretrained.ckpt.

3. Generate Transition State

Run the generation script with reactant and product files (xyz, cif, pdb, etc. - anything ASE reads).

# Env: react-ot-agent
python .agents/skills/chem-react-ot/scripts/generate_ts.py \
    --reactants reactant.xyz \
    --products product.xyz \
    --output_dir results/ts_search

Arguments:

--reactants: Path to reactant structure file(s). Can be a single file with multiple molecules or a list of files.
--products: Path to product structure file(s).
--output_dir: Directory to save the generated TS structure (ts_generated.xyz) and trajectory (generation_traj.xyz).
--nfe: Number of function evaluations for the ODE solver (default: 10). Higher values might be more accurate but slower.
--checkpoint: Path to custom model checkpoint (optional, defaults to downloaded one).

Example

# Env: react-ot-agent
python .agents/skills/chem-react-ot/scripts/generate_ts.py \
    --reactants .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/reactant.xyz \
    --products .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/product.xyz \
    --output_dir .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/output

Constraints

Environment: All scripts require the react-ot-agent conda environment.
Input Format: Reactant and product structures must be in any format readable by ASE (XYZ, CIF, PDB, etc.).
Atom Ordering: Reactant and product structures must have the same number of atoms with consistent atom ordering.
Model Checkpoint: The pre-trained checkpoint must be downloaded before first use (see step 2).

References

React-OT GitHub
Duan, C., Liu, G.-H., Du, Y. et al., "Optimal transport for generating transition states in chemical reactions", Nature Machine Intelligence, 2025. DOI

Author: Bowen Deng Contact: GitHub @learningmatter-mit

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Métadonnées du fichier

name: chem-react-ot
description: Generate transition state structures for chemical reactions using React-OT.
category: [chemistry]

Voir le texte original

---
name: chem-react-ot
description: Generate transition state structures for chemical reactions using React-OT.
category: [chemistry]
---

# `chem-react-ot` — React-OT Transition State Generation

## Goal

Generate transition state (TS) structures given reactant and product structures using the React-OT model (Optimal Transport). React-OT is a generative model that predicts TS geometries directly without requiring an initial guess path (like NEB).

**Category:** `chemistry`
**Environment:** `react-ot-agent`

## Key Features

- **Generative TS Prediction:** Predicts 3D transition state structures from 3D reactants and products.
- **Fast Inference:** Uses an ODE solver for generation, typically much faster than DFT-based NEB.
- **No Path Guess Required:** Directly generates the TS structure.

## Usage

### 1. Environment Setup

This skill requires the `react-ot-agent` conda environment. Ensure it is installed:

```bash
# Env: react-ot-agent
cd conda-envs/react-ot-agent
bash install.sh
```

### 2. Download Models

Before running the skill for the first time, download the pre-trained model weights:

```bash
# activate react-ot-agent first
conda activate react-ot-agent
python conda-envs/react-ot-agent/download_models.py
```

The checkpoint is saved to `~/.cache/react-ot/checkpoints/sb-pretrained.ckpt`.

### 3. Generate Transition State

Run the generation script with reactant and product files (xyz, cif, pdb, etc. - anything ASE reads).

```bash
# Env: react-ot-agent
python .agents/skills/chem-react-ot/scripts/generate_ts.py \
    --reactants reactant.xyz \
    --products product.xyz \
    --output_dir results/ts_search
```

**Arguments:**

- `--reactants`: Path to reactant structure file(s). Can be a single file with multiple molecules or a list of files.
- `--products`: Path to product structure file(s).
- `--output_dir`: Directory to save the generated TS structure (`ts_generated.xyz`) and trajectory (`generation_traj.xyz`).
- `--nfe`: Number of function evaluations for the ODE solver (default: 10). Higher values might be more accurate but slower.
- `--checkpoint`: Path to custom model checkpoint (optional, defaults to downloaded one).

## Example

```bash
# Env: react-ot-agent
python .agents/skills/chem-react-ot/scripts/generate_ts.py \
    --reactants .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/reactant.xyz \
    --products .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/product.xyz \
    --output_dir .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/output
```

## Constraints

- **Environment**: All scripts require the `react-ot-agent` conda environment.
- **Input Format**: Reactant and product structures must be in any format readable by ASE (XYZ, CIF, PDB, etc.).
- **Atom Ordering**: Reactant and product structures must have the same number of atoms with consistent atom ordering.
- **Model Checkpoint**: The pre-trained checkpoint must be downloaded before first use (see step 2).

## References

- [React-OT GitHub](https://github.com/deepprinciple/react-ot)
- Duan, C., Liu, G.-H., Du, Y. et al., "Optimal transport for generating transition states in chemical reactions", *Nature Machine Intelligence*, 2025. [DOI](https://doi.org/10.1038/s42256-025-01010-0)

---

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

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Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
The script's handling of multiple reactant/product files is not fully documented; the example only shows single files, while the description mentions support for lists.
The model weight download step relies on an external URL without explicit checksum verification, which could be a supply chain risk if the source is compromised.
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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Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: learningmatter-mit/AtomisticSkills
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 3 sept. 2026
Registre mis à jour: 6 sept. 2026
Chemin des instructions: .agents/skills/chem-react-ot/SKILL.md @ d1f7ecfda2cd

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

66/100

Prometteur

Confiance

56/100

Do not auto-install

Audit

73/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
The script's handling of multiple reactant/product files is not fully documented; the example only shows single files, while the description mentions support for lists.
The model weight download step relies on an external URL without explicit checksum verification, which could be a supply chain risk if the source is compromised.
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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Source: learningmatter-mit/AtomisticSkills
Indexé par: Index communautaire OpenAgentSkill

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