ai2-kit (pronounced as "AI Squared Kit") is a command line and python toolkit featured artificial intelligence × ab initio for complex chemistry systems research.
[!NOTE] Our
ai2-kitpaper has been submitted as a preprint: arXiv:2607.00613. The paper-related examples are available under example/supplement.
Feature Highlights
- 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.
- Provide reusable skills under ./skills for building code related to
ai2-kit.- build-tesla: a skill for building TESLA workflows from existing examples.
Environment Requirements
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.
Installation
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
Usage
All ai2-kit commands can be accessed via the command line interface ai2-kit.
You can run the following command to see the available commands and sub-commands:
ai2-kit
# e.g.
ai2-kit tool
ai2-kit tool dpdata
For detailed usage of each command, you can run the following command to view the help information:
ai2-kit <sub-command> --help
# e.g.
ai2-kit tool dpdata read --help
Manuals
General Tools
- 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
Workflows
Example Driven Workflows (Recommended)
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.
Config Driven Workflows
[!WARNING] Deprecated: Config-driven workflows are deprecated and will be maintained only for backward compatibility. For new projects and future updates, please use the example-driven workflows above, which will be the primary approach going forward.
These workflows are driven by configuration files, which can be easily modified to fit your own research purpose.
Featuring Tools
- 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.
Online Apps and Notebooks
- NMRPeak Online App: AI tools for NMR analysis.
- Electrolyte Designer: run electrolyte simulations with ease.
- NMRNet Prediction: an online app to predict NMR chemical shifts with pre-trained NMRNet models.
Tips
- Tips: useful tips for using
ai2-kit
1 maintainer
Metadata
1 maintainer
Release files for ai2-kit 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ai2_kit-1.1.0.tar.gz | 148.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ai2_kit-1.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 348.9 kB
Release files / ai2_kit-1.1.0.tar.gz
| Download URL | ai2_kit-1.1.0.tar.gz |
|---|---|
| Size | 148.5 kB |
| Tags | Source |
|
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No |
| Uploaded via |
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|
Release files / ai2_kit-1.1.0-py3-none-any.whl
| Download URL | ai2_kit-1.1.0-py3-none-any.whl |
|---|---|
| Size | 200.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.4.1 CPython/3.10.20 Linux/6.18.35.2-microsoft-standard-WSL2
|
