Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

12 August 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding our ever-growing populations to increasing our 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 have revolutionized a whole new way to approach data- intensive problems, and many of these developments have started to enter chemistry. However, similar progress in the field of homogenous catalysis are only in their infancy. In this article, we want to outline our vision for the future of catalyst design and the role of machine learning to navigate this maze.


machine learning
Artificial Intelligence
Computational Chemistry
High-Throughput Analysis

Supplementary materials

Figure 4 maze


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