chem-react-ot

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:

# 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

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Author: Bowen Deng Contact: GitHub @learningmatter-mit