learningmatter-mit
Indexado en Registry
chem-react-ot
Generate transition state structures for chemical reactions using React-OT.
Resumen
Generate transition state structures for chemical reactions using React-OT.
Leer documentación completa
Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.
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-agentconda 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
Metadatos del archivo
name: chem-react-ot description: Generate transition state structures for chemical reactions using React-OT. category: [chemistry]
Ver texto 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)
Revisar el código fuente
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- Licencia
- MIT
- Precio sin confirmar
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Fuente del skill registrada
La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.
Revisar antes de instalar: Evitar instalación automática
Licencia: 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
Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.
Empieza con una tarea pequeña
- 1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
- 2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
- 3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.
Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.
Fuente y notas de uso
Indexado
Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.
- 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-react-ot/SKILL.md @ d1f7ecfda2cd
Versión declarada en el registro; consulta las versiones de la fuente.
Calidad
66/100
Prometedor
Confianza
56/100
Do not auto-install
Auditoría
73/100
Requiere revisión
- 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
- Verified installs
- —
- Resultados
- —
Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.
Acceso para agentes
La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.
Más detalles
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