Summit: Benchmarking Machine Learning Methods for Reaction Optimisation
Summit: Benchmarking Machine Learning Methods for Reaction Optimisation
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DOI：
10.1002/cmtd.202000051
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发表时间：
2021-02-01
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影响因子：
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通讯作者：
Lapkin, Alexei A.
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文献类型：
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作者：
Felton, Kobi C.;Rittig, Jan G.;Lapkin, Alexei A.
在精细化工行业，反应筛选和优化对于新产品的开发至关重要。然而，这种筛选可能是非常耗时和劳动密集型的，特别是当使用直觉时。机器学习通过基于过去的实验数据迭代建议新实验来提供解决方案，但知道在特定情况下应用哪种机器学习策略仍然很困难。在这里，我们开发了化学动机的虚拟基准反应优化和比较这些基准的几种策略。基准和战略包含在一个名为Summit的开源框架中。我们的测试结果表明，贝叶斯优化策略在化学反应优化中所面临的问题类型中表现得非常好，而反应优化中常用的许多策略未能找到最佳解决方案。
In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past experimental data, but knowing which machine learning strategy to apply in a particular case is still difficult. Here, we develop chemically-motivated virtual benchmarks for reaction optimisation and compare several strategies on these benchmarks. The benchmarks and strategies are encompassed in an open-source framework named Summit. The results of our tests show that Bayesian optimisation strategies perform very well across the types of problems faced in chemical reaction optimisation, while many strategies commonly used in reaction optimisation fail to find optimal solutions.