A toolkit featured artificial intelligence × ab initio for computational chemistry research.
Please be advised that ai2-kit is still under heavy development and you should expect things to change often. We encourage people to play and explore with ai2-kit, and stay tuned with us for more features to come.
- Collection of tools to facilitate the development of automated workflows for computational chemistry research.
- Use with oh-my-batch to build your own workflow with shell script.
ai2-kit is developed and tested on Linux systems, and it should work on macOS as well.
For Windows, most of the ai2-kit features are expected to work. However, some third-party libraries (such as dscribe) may not function properly on Windows.
In such cases, it is recommended to use ai2-kit through Windows Subsystem for Linux (WSL).
If you are using the latest version of Python, some third-party libraries may not yet provide pre-built binary releases,
which can cause pip install to fail. It is therefore suggested to use ai2-kit with Python 3.10–3.12.
We strongly recommend creating a dedicated Conda environment to avoid unexpected issues caused by incompatible package versions.
You can use the following command to install ai2-kit:
# for users who just use most common features
pip install ai2-kit
# for users who want to use all features
pip install ai2-kit[all]If you want to run ai2-kit from source, you can run the following commands in the project folder:
pip install poetry
# If you meet ConnectionError, you can try to set the max-workers to a smaller number, e.g
# poetry config installer.max-workers 4
poetry install
poetry run ai2-kit- NMRNet: A toolkit for predict NMR with deep learning network.
- Proton Transfer Analysis Toolkit
- Amorphous Oxides Structure Analysis Toolkit
- Re-weighting Toolkit
- ai2-cat: A toolkit for dynamic catalysis researching.
These workflows are built with oh-my-batch and example shell scripts, which can be easily adapted to your own research purpose. It provides more flexibility and transparency to run and customize their own workflows.
- TESLA: A customizable active learning workflow for training machine learning potentials.
- TESLA PIMD: A customizable active learning workflow for training machine learning potentials with path integral molecular dynamics.
- TESLA for ec-MLP: A customizable active learning workflow for training machine learning potentials for electrolyte systems.
These workflows are driven by configuration files, which can be easily modified to fit your own research purpose.
- ASE Toolkit: commands to process trajectory files with ASE
- DPData Toolkit: commands to process system data with dpdata
- Model Deviation Toolkit: a toolkit to filter structures by model deviation
- Electrolyte Designer: run electrolyte simulations with ease.
- NMRNet Prediction: an online app to predict NMR chemical shifts with pre-trained NMRNet models.
- ai2cat: an interactive notebook for dynamic catalysis research.
- Tips: useful tips for using
ai2-kit