BOLUDO 🔮

Code accompanying the paper A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes

Bayesian Optimization for nanocrystaL strUcture Design Optimization
But really, it's the work of Bojana Ranković and Ludovic Zaza, under the supervision of Prof. Raffaella Buonsanti and Prof. Philippe Schwaller, bringing Bayesian optimization into the chemistry lab and discovering new nanocrystal morphologies! 🚀

Also, we fully acknowledge the meaning of "boludo" in Argentine Spanish slang - turns out even crystals can be shaped by a couple of friendly boludos. 😂

🎯 Overview

Figure 1: Complete workflow showing ELN integration, BO model fitting, and experimental validation

BOLUDO is a machine learning framework that revolutionizes nanocrystal synthesis by:

• Predicting nanocrystal shapes from reaction conditions
• Suggesting optimal reaction parameters for target shapes
• Operating effectively with limited data (<200 experimental points)
• Enabling discovery of new nanocrystal shapes through continuous energy scale mapping

✨ Model Architecture

The system consists of three main components:

1. Data Processing Pipeline

   • ELN data extraction
   • Feature engineering
   • Synthesis parameter standardization

2. Machine Learning Models

   • Random Forest for interpretable predictions
   • Gaussian Process for Bayesian optimization
   • Surface energy scale mapping

3. Optimization Framework

   • Bayesian optimization for parameter space exploration
   • Multi-objective optimization capabilities
   • Uncertainty quantification

Figure 2: Visualization of parameter importance in nanocrystal shape prediction

🏆 Results

Our framework has achieved significant breakthroughs:

• Successfully predicted synthesis conditions for various Cu nanocrystal shapes and vice versa
• Discovered novel synthesis pathways
• Achieved first-time synthesis of Cu rhombic dodecahedron shape
• Demonstrated effectiveness with only 115 initial data points

Figure 1: Complete workflow showing ELN integration, BO model fitting, and experimental validation

🚀 Installation

The most recent code and data can be installed directly from GitHub with:

$ pip install git+https://github.com/schwallergroup/boludo.git

📚 Citation

@article{zaza2024holistic,
  title={A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes},
  author={Zaza, Ludovic and Rankovic, Bojana and Schwaller, Philippe and Buonsanti, Raffaella},
  journal={Journal of the American Chemical Society},
  year={2024},
  doi={10.1021/jacs.4c17283}
}

💰 Acknowledgments

This work was supported by NCCR Catalysis, a National Centre of Competence in Research funded by the Swiss National Science Foundation (grant number 180544).

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

👋 Attribution

⚖️ License

The code in this package is licensed under the MIT License.

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.

🛠️ For Developers

See developer instructions

The final section of the README is for if you want to get involved by making a code contribution.

Development Installation

To install in development mode, use the following:

$ git clone git+https://github.com/schwallergroup/boludo.git
$ cd boludo
$ pip install -e .

🥼 Testing

After cloning the repository and installing tox with pip install tox, the unit tests in the tests/ folder can be run reproducibly with:

$ tox

Additionally, these tests are automatically re-run with each commit in a GitHub Action.

📖 Building the Documentation

The documentation can be built locally using the following:

$ git clone git+https://github.com/schwallergroup/boludo.git
$ cd boludo
$ tox -e docs
$ open docs/build/html/index.html

The documentation automatically installs the package as well as the docs extra specified in the setup.cfg. sphinx plugins like texext can be added there. Additionally, they need to be added to the extensions list in docs/source/conf.py.

📦 Making a Release

After installing the package in development mode and installing tox with pip install tox, the commands for making a new release are contained within the finish environment in tox.ini. Run the following from the shell:

$ tox -e finish

This script does the following:

1. Uses Bump2Version to switch the version number in the setup.cfg, src/boludo/version.py, and docs/source/conf.py to not have the -dev suffix
2. Packages the code in both a tar archive and a wheel using build
3. Uploads to PyPI using twine. Be sure to have a .pypirc file configured to avoid the need for manual input at this step
4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped.
5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can use tox -e bumpversion -- minor after.