Bayesian Optimization for Chemical Reactions.
Bayesian Optimization for Chemical Reactions.
复制标题
化学反应的贝叶斯优化。
DOI：
10.2533/chimia.2023.31
复制
发表时间：
2023
期刊：
影响因子：
1.2
通讯作者：
P. Schwaller
中科院分区：
文献类型：
--
作者：
Jeff Guo;Bojana Ranković;P. Schwaller
反应优化具有挑战性，传统上委托给领域专家，他们迭代地提出越来越优化的实验。问题是，反应景观是复杂的，往往需要数百个实验才能达到收敛，这意味着巨大的资源汇。贝叶斯优化（BO）是一种基于先前观察结果推荐下一个实验的优化算法，最近在普通化学界引起了相当大的兴趣。BO在化学反应中的应用已被证明可以提高优化活动的效率，并可以在许多可能性中推荐有利的反应条件。此外，它能够联合优化所需的目标，如产率和立体选择性，使其成为一个有吸引力的替代或至少补充领域专家指导的优化。随着BO软件的民主化，将BO应用于化学反应的门槛大大降低。两种范式之间的交叉点将以前所未有的速度取得进步。在这篇综述中，我们讨论了如何将化学反应转化为机器可读的格式，这些格式可以通过机器学习（ML）模型来学习。我们提出了BO的基础，以及它如何已经被应用于优化化学反应的结果。我们传达的重要信息是，实现ML增强反应优化的全部潜力将需要实验学家和计算科学家之间的密切合作。
Reaction optimization is challenging and traditionally delegated to domain experts who iteratively propose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence, representing an enormous resource sink. Bayesian optimization (BO) is an optimization algorithm that recommends the next experiment based on previous observations and has recently gained considerable interest in the general chemistry community. The application of BO for chemical reactions has been demonstrated to increase efficiency in optimization campaigns and can recommend favorable reaction conditions amidst many possibilities. Moreover, its ability to jointly optimize desired objectives such as yield and stereoselectivity makes it an attractive alternative or at least complementary to domain expert-guided optimization. With the democratization of BO software, the barrier of entry to applying BO for chemical reactions has drastically lowered. The intersection between the paradigms will see advancements at an ever-rapid pace. In this review, we discuss how chemical reactions can be transformed into machine-readable formats which can be learned by machine learning (ML) models. We present a foundation for BO and how it has already been applied to optimize chemical reaction outcomes. The important message we convey is that realizing the full potential of ML-augmented reaction optimization will require close collaboration between experimentalists and computational scientists.