Alexandre A. Schoepfer, Jan Weinreich, Ruben Laplaza, Jerome Waser and Clemence Corminboeuf
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引用次数: 0
摘要
贝叶斯优化法(BO)是解决复杂优化问题的有效方法,在化学研究领域也越来越受欢迎。尽管贝叶斯优化法能有效指导实验设计,但它并不考虑实验成本:在不同条件下测试现成的试剂可能比合成或购买额外的试剂更节省成本和时间。为解决这一问题,我们提出了成本知情的 BO(CIBO),这是一种为合理规划化学实验而量身定制的方法,可优先考虑最具成本效益的实验。只有当试剂对反应性能的预期改善足以超过其成本时,才会使用。我们的算法跟踪可用试剂,包括最近获得的试剂,并在优化过程中动态更新其成本。通过使用钯催化反应的文献数据,我们发现与标准 BO 相比,CIBO 可将反应优化成本最多降低 90%。我们的方法与任何类型的成本兼容,例如购买设备或化合物的成本、等待时间以及环境或安全问题。我们相信,CIBO 拓展了 BO 在化学领域的应用前景,并设想将其应用于传统实验室和自动驾驶实验室的实验规划。
Bayesian optimization (BO) is an efficient method for solving complex optimization problems, including those in chemical research, where it is gaining significant popularity. Although effective in guiding experimental design, BO does not account for experimentation costs: testing readily available reagents under different conditions could be more cost and time-effective than synthesizing or buying additional ones. To address this issue, we present cost-informed BO (CIBO), an approach tailored for the rational planning of chemical experimentation that prioritizes the most cost-effective experiments. Reagents are used only when their anticipated improvement in reaction performance sufficiently outweighs their costs. Our algorithm tracks available reagents, including those recently acquired, and dynamically updates their cost during the optimization. Using literature data of Pd-catalyzed reactions, we show that CIBO reduces the cost of reaction optimization by up to 90% compared to standard BO. Our approach is compatible with any type of cost, e.g., of buying equipment or compounds, waiting time, as well as environmental or security concerns. We believe CIBO extends the possibilities of BO in chemistry and envision applications for both traditional and self-driving laboratories for experiment planning.