Installation

moTSart uses conda (xTB is distributed via conda-forge). Installing Miniforge is the recommended setup.

0. Install Miniforge on Linux:

wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
bash Miniforge3-Linux-x86_64.sh
~/miniforge3/bin/conda init bash
exec bash

0. Install Miniforge on macOS (Apple Silicon):

curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
chmod +x Miniforge3-MacOSX-arm64.sh
bash Miniforge3-MacOSX-arm64.sh
exec zsh

1. Clone the moTSart repository:

git clone https://github.com/heid-lab/motsart.git
cd motsart

2. Create environment, activate it, and install moTSart in editable mode:

conda env create -f environment.yml
conda activate motsart
pip install -e .

Optional dependencies:

# Reaction path optimization
pip install pysisyphus

# GPU DFT validation (Linux, optional)
pip install pyscf gpu4pyscf-cuda12x

PyTorch + PyG for learning:

# Linux (CUDA 12.4)
pip install --index-url https://download.pytorch.org/whl/cu124 'torch==2.6.0' 'torchvision==0.21.0'
pip install -f https://data.pyg.org/whl/torch-2.6.0+cu124.html pyg-lib torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric

# macOS (CPU)
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv torch_geometric -f https://data.pyg.org/whl/torch-2.6.0+cpu.html

Usage

Please check out the documentation for a comprehensive user guide: heid-lab.github.io/motsart

Configure

We use hydra-zen for managing configurations directly in python configuration files. Before running the pipeline, make sure you set all the paths in the environment configuration file under src/motsart/conf.py. This includes the paths to software such as xTB or ORCA, as well as the output directory where results will be written. These configuration files for the modules complex_finder, path_guessers, validator, and learning are found in their respective module folders and are named conf.py

Run

• Run the full pipeline locally: bash complex_and_ts_search_local.sh
• Run the pipeline on SLURM CPU nodes: sbatch complex_and_ts_search_cpu.sh

Analysis

Pipeline artifacts (geometries, paths, validation outputs) are written per reaction under the configured results directory.