https://github.com/aspuru-guzik-group/golem

Golem: an algorithm for robust experiment and process optimization

https://github.com/aspuru-guzik-group/golem

Science Score: 20.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
    Links to: arxiv.org
  • Committers with academic emails
    3 of 4 committers (75.0%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.5%) to scientific vocabulary

Keywords

experimental-design machine-learning optimization
Last synced: 10 months ago · JSON representation

Repository

Golem: an algorithm for robust experiment and process optimization

Basic Info
Statistics
  • Stars: 18
  • Watchers: 4
  • Forks: 1
  • Open Issues: 3
  • Releases: 0
Topics
experimental-design machine-learning optimization
Created over 6 years ago · Last pushed over 5 years ago
Metadata Files
Readme License

README.md

Golem: An algorithm for robust experiment and process optimization

Build Status codecov

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.

Owner

  • Name: Aspuru-Guzik group repo
  • Login: aspuru-guzik-group
  • Kind: organization

GitHub Events

Total
  • Watch event: 1
  • Fork event: 1
Last Year
  • Watch event: 1
  • Fork event: 1

Committers

Last synced: over 3 years ago

All Time
  • Total Commits: 186
  • Total Committers: 4
  • Avg Commits per committer: 46.5
  • Development Distribution Score (DDS): 0.468
Top Committers
Name Email Commits
Matteo Aldeghi m****h@m****e 99
matteoaldeghi m****i@v****i 76
Matteo Aldeghi m****i@v****i 6
Flo h****n@g****m 5
Committer Domains (Top 20 + Academic)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 74 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 1
  • Total maintainers: 1
pypi.org: matter-golem

Golem: An Algorithm for Robust Experiment and Process Optimization

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 74 Last month
Rankings
Dependent packages count: 10.1%
Stargazers count: 16.0%
Dependent repos count: 21.6%
Average: 27.0%
Forks count: 29.8%
Downloads: 57.3%
Maintainers (1)
Last synced: 11 months ago

Dependencies

docsrc/requirements.txt pypi
  • m2r2 *
  • msmb_theme *
  • nbsphinx *
  • pandoc *
  • sphinx *
  • sphinx_rtd_theme *
setup.py pypi
  • numpy *
  • pandas *
  • scikit-learn *
  • scipy >=1.4