Golem: An algorithm for robust experiment and process optimization

Golem is an algorithm for robust optimization. It can be used in conjunction with any optimization algorithms or design of experiment strategy of choice. Golem helps identifying optimal solutions that are robust to input uncertainty, thus ensuring the reproducible performance of optimized experimental protocols and processes. It can be used to analyze the robustness of past experiments, or to guide experiment planning algorithms toward robust solutions on the fly. For more details on the algorithm and its behaviour please refer to the publication and the documentation.

Installation

Golem can be installed with pip:

pip install matter-golem

Dependencies

The installation requires:

• python >= 3.7
• numpy
• scipy >= 1.4
• pandas
• scikit-learn

Citation

Golem is research software. If you make use of it in scientific publications, please cite the following article:

@misc{golem,
      title={Golem: An algorithm for robust experiment and process optimization}, 
      author={Matteo Aldeghi and Florian Häse and Riley J. Hickman and Isaac Tamblyn and Alán Aspuru-Guzik},
      year={2021},
      eprint={2103.03716},
      archivePrefix={arXiv},
      primaryClass={math.OC}
      }

License

Golem is distributed under an MIT License.