BoFire: Bayesian Optimization Framework Intended for Real Experiments
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| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Dürholt, Johannes P. Asche, Thomas S. Kleinekorte, Johanna Mancino-Ball, Gabriel Schiller, Benjamin Sung, Simon Keupp, Julian Osburg, Aaron Boyne, Toby Misener, Ruth Eldred, Rosona Costa, Wagner Steuer Kappatou, Chrysoula Lee, Robert M. Linzner, Dominik Walz, David Wulkow, Niklas Shafei, Behrang |
| author_facet | Dürholt, Johannes P. Asche, Thomas S. Kleinekorte, Johanna Mancino-Ball, Gabriel Schiller, Benjamin Sung, Simon Keupp, Julian Osburg, Aaron Boyne, Toby Misener, Ruth Eldred, Rosona Costa, Wagner Steuer Kappatou, Chrysoula Lee, Robert M. Linzner, Dominik Walz, David Wulkow, Niklas Shafei, Behrang |
| contents | Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Previous BO implementations, for example as they exist in the literature or software, require substantial adaptation for effective real-world deployment in chemical industry. BoFire provides a rich feature-set with extensive configurability and realizes our vision of fast-tracking research contributions into industrial use via maintainable open-source software. Owing to quality-of-life features like JSON-serializability of problem formulations, BoFire enables seamless integration of BO into RESTful APIs, a common architecture component for both self-driving laboratories and human-in-the-loop setups. This paper discusses the differences between BoFire and other BO implementations and outlines ways that BO research needs to be adapted for real-world use in a chemistry setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05040 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BoFire: Bayesian Optimization Framework Intended for Real Experiments Dürholt, Johannes P. Asche, Thomas S. Kleinekorte, Johanna Mancino-Ball, Gabriel Schiller, Benjamin Sung, Simon Keupp, Julian Osburg, Aaron Boyne, Toby Misener, Ruth Eldred, Rosona Costa, Wagner Steuer Kappatou, Chrysoula Lee, Robert M. Linzner, Dominik Walz, David Wulkow, Niklas Shafei, Behrang Machine Learning Optimization and Control Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Previous BO implementations, for example as they exist in the literature or software, require substantial adaptation for effective real-world deployment in chemical industry. BoFire provides a rich feature-set with extensive configurability and realizes our vision of fast-tracking research contributions into industrial use via maintainable open-source software. Owing to quality-of-life features like JSON-serializability of problem formulations, BoFire enables seamless integration of BO into RESTful APIs, a common architecture component for both self-driving laboratories and human-in-the-loop setups. This paper discusses the differences between BoFire and other BO implementations and outlines ways that BO research needs to be adapted for real-world use in a chemistry setting. |
| title | BoFire: Bayesian Optimization Framework Intended for Real Experiments |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2408.05040 |