Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

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.