--- 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)

--- 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)

--- 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)

--- 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)