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 workflow: A customizable active learning workflow for training machine learning potentials.
- TESLA PIMD workflow: A customizable active learning workflow for training machine learning potentials with path integral molecular dynamics.
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