The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding the ever-growing population to increasing life expectancies through the synthesis of new drugs. However, developing new chemical reactions and catalytic systems is a tedious task that requires tremendous discovery and optimization efforts. Over the past decade, advances in machine learning (ML) have revolutionized a whole new way to approach data-intensive problems, and many of these developments have started to enter chemistry. Meanwhile, similar advances in the field of homogeneous catalysis are in only their infancy. In this perspective, we outline our vision for the future of homogeneous catalyst design and the role of ML in navigating this maze.

通过催化形成困难的化学键的能力已经改变了各个方面的社会，从养活不断增长的人口到通过合成新药来增加预期寿命。但是，开发新的化学反应和催化系统是一项繁琐的任务，需要大量的发现和优化工作。在过去的十年中，机器学习（ML）的进步彻底改变了解决数据密集型问题的全新方式，其中许多发展已开始进入化学领域。同时，在均相催化领域的类似进展仅在其初期。从这个角度出发，我们概述了对均相催化剂设计的未来愿景以及ML在迷宫导航中的作用。