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Bayesian optimization for chemical reactions

delete2026-04-16
delete0
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OA
AI
S
Stefan Desimpel *
M
Matthieu Dorbec
K
Kevin M. Van Geem
C
Christian V. Stevens
DOI：10.1039/D5CS00962Fdelete
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摘要

摘要

En 中文
贝叶斯优化（BO）通过在大规模混合变量参数空间中平衡探索与利用，实现复杂化学反应的数据高效优化。本文为希望采用BO的化学家提供了易于理解的介绍，概述了代理模型、采集函数和关键数学概念的基本原理。强调实用性考虑，包括核设计、分类变量表示以及多目标与批优化的策略。应用全面综述了从高通量平台到自动化流反应器及更大规模工艺的实验尺度。最后，讨论了迁移学习和数据复用等新兴方向，以加速优化任务并促进化学领域更具通用性的数据驱动策略。
Keyword:
Bayesian optimization
chemical reactions
surrogate models
acquisition functions
multi-objective optimization

期刊

C
chem. soc. rev.
IF:
0
论文数:
125
被引数:
0

机构

G
Ghent University
学者数:
5.2W
论文数: 4.5W
被引数: 5.5W
J
janssen r&d
学者数:
28
论文数: 9
被引数: 5
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