# BoFire: Bayesian Optimization Framework Intended for Real Experiments

arXiv: https://arxiv.org/abs/2408.05040 · HTML: https://mlsanity.com/paper/2408.05040/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [Unexpected Improvements to Expected Improvement for Bayesian Optimization](https://mlsanity.com/paper/2310.20708/) (2023)
- [Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement](https://mlsanity.com/paper/2105.08195/) (2021)
- [Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly](https://mlsanity.com/paper/1903.06694/) (2019)
- [GAUCHE: A Library for Gaussian Processes in Chemistry](https://mlsanity.com/paper/2212.04450/) (2022)
- [Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization](https://mlsanity.com/paper/2211.06149/) (2022)
- [ENTMOOT: A Framework for Optimization over Ensemble Tree Models](https://mlsanity.com/paper/2003.04774/) (2020)
- [Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax](https://mlsanity.com/paper/1912.05686/) (2019)

## Built on by (most cited first, 6 of 6)

- [BARK: A Fully Bayesian Tree Kernel for Black-box Optimization](https://mlsanity.com/paper/2503.05574/) (2025)
- [Honegumi: An Interface for Accelerating the Adoption of Bayesian Optimization in the Experimental Sciences](https://mlsanity.com/paper/2502.06815/) (2025)
- [A Guide to Bayesian Optimization in Bioprocess Engineering](https://mlsanity.com/paper/2508.10642/) (2025)
- [CARE: Context-Aware Ranking Evolution with Executable Scoring Programs for Budgeted Reaction Optimization](https://mlsanity.com/paper/2606.14581/) (2026)
- [La Agente 'Optima: Towards Agentic Self-Driving Laboratories](https://mlsanity.com/paper/2609.04564/) (2026)
- [mlr3mbo: Bayesian Optimization in R](https://mlsanity.com/paper/2603.29730/) (2026)

## Abstract (arXiv)

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.