Reaching optimal reaction conditions is crucial to achieve high yields, minimal by-products, and environmentally sustainable chemical reactions. With the recent rise of artificial intelligence, there has been a shift from traditional Edisonian trial-and-error optimization to data-driven and automated approaches, which offer significant advantages. Here, we showcase the capabilities of an integrated platform; we conducted simultaneous optimizations of four different terminal alkynes and two reaction routes using an automation platform combined with a Bayesian optimization platform. Remarkably, we achieved a conversion rate of over 80% for all four substrates in 23 experiments, covering ca. 0.2% of the combinatorial space. Further analysis allowed us to identify the influence of different reaction parameters on the reaction outcomes, demonstrating the potential for expedited reaction condition optimization and the prospect of more efficient chemical processes in the future.

中文翻译：

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结合贝叶斯优化和自动化，同时优化反应条件和路线

达到最佳反应条件对于实现高产率、最少副产物和环境可持续的化学反应至关重要。随着人工智能最近的兴起，传统的爱迪生式试错优化已经转变为数据驱动和自动化方法，这提供了显着的优势。在这里，我们展示了集成平台的功能；我们使用自动化平台与贝叶斯优化平台相结合，对四种不同的末端炔烃和两条反应路线进行了同步优化。值得注意的是，我们在 23 次实验中实现了所有四种底物超过 80% 的转化率，覆盖了大约10% 的转化率。组合空间的 0.2%。进一步的分析使我们能够确定不同反应参数对反应结果的影响，展示了加速反应条件优化的潜力以及未来更高效化学过程的前景。