BoFire: Bayesian Optimization Framework Intended for Real Experiments
Authors
Research Topics
Paper Information
-
Journal:
Journal of Machine Learning Research -
Added to Tracker:
Dec 30, 2025
Abstract
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.
Author Details
Johannes P. Dürholt
AuthorThomas S. Asche
AuthorJohanna Kleinekorte
AuthorGabriel Mancino-Ball
AuthorBenjamin Schiller
AuthorSimon Sung
AuthorJulian Keupp
AuthorAaron Osburg
AuthorToby Boyne
AuthorRuth Misener
AuthorRosona Eldred
AuthorChrysoula Kappatou
AuthorRobert M. Lee
AuthorDominik Linzner
AuthorWagner Steuer Costa
AuthorDavid Walz
AuthorNiklas Wulkow
AuthorBehrang Shafei
AuthorResearch Topics & Keywords
Computational Statistics
Research AreaBayesian Statistics
Research AreaCitation Information
APA Format
Johannes P. Dürholt
,
Thomas S. Asche
,
Johanna Kleinekorte
,
Gabriel Mancino-Ball
,
Benjamin Schiller
,
Simon Sung
,
Julian Keupp
,
Aaron Osburg
,
Toby Boyne
,
Ruth Misener
,
Rosona Eldred
,
Chrysoula Kappatou
,
Robert M. Lee
,
Dominik Linzner
,
Wagner Steuer Costa
,
David Walz
,
Niklas Wulkow
&
Behrang Shafei
.
BoFire: Bayesian Optimization Framework Intended for Real Experiments.
Journal of Machine Learning Research
.
BibTeX Format
@article{paper726,
title = { BoFire: Bayesian Optimization Framework Intended for Real Experiments },
author = {
Johannes P. Dürholt
and Thomas S. Asche
and Johanna Kleinekorte
and Gabriel Mancino-Ball
and Benjamin Schiller
and Simon Sung
and Julian Keupp
and Aaron Osburg
and Toby Boyne
and Ruth Misener
and Rosona Eldred
and Chrysoula Kappatou
and Robert M. Lee
and Dominik Linzner
and Wagner Steuer Costa
and David Walz
and Niklas Wulkow
and Behrang Shafei
},
journal = { Journal of Machine Learning Research },
url = { https://www.jmlr.org/papers/v26/24-1540.html }
}