Recent literature suggests that the fields of machine learning (ML) and high-throughput experimentation (HTE) have separately received considerable attention from chemists and engineers, leading to the development of powerful reactivity models and platforms capable of rapidly performing thousands of reactions. The merger of ML with HTE presents a wealth of opportunities for the exploration of chemical space, but the integration of the two has yet to be fully realized. We highlight examples of recent developments in ML and HTE that collectively suggest the utility of their integration. Our analysis highlights the complementarity of the two fields, while exposing a number of obstacles that can and should be overcome to take full advantage of this merger and thereby accelerate chemical research.

最近的文献表明，机器学习（ML）和高通量实验（HTE）领域已经分别受到化学家和工程师的关注，从而导致了功能强大的反应性模型和平台的开发，这些模型和平台能够快速执行数千个反应。ML与HTE的合并为探索化学空间提供了很多机会，但两者的整合尚未完全实现。我们重点介绍ML和HTE近期发展的一些例子，这些例子共同说明了它们集成的实用性。我们的分析突出显示了这两个领域的互补性，同时暴露了可以并应克服的许多障碍，以充分利用此次合并并从而加速化学研究。