BoFire: Bayesian Optimization Framework Intended for Real Experiments
arxiv(2024)
摘要
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.
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