BoFire: Bayesian Optimization Framework Intended for Real Experiments

TL;DR

BoFire is an open-source Python framework that integrates Bayesian Optimization with other experimental design strategies, tailored for real-world chemical research and industrial applications.

Contribution

It introduces a highly configurable, maintainable BO framework optimized for chemical experiments, facilitating seamless integration into industrial workflows.

Findings

Supports JSON-serializable problem formulations

Enables integration into RESTful APIs for automation

Addresses adaptation needs of BO for chemical industry

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.