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Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

submitted on 12.08.2020, 12:39 and posted on 12.08.2020, 15:07 by Gabriel dos Passos Gomes, Robert Pollice, Alan Aspuru-Guzik

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.


Natural Sciences and Engineering Research Council of Canada (NSERC) Banting Fellowship

Defense Advanced Research Projects Agency (DARPA) under the Accelerated Molecular Discovery Program under Cooperative Agreement No. HR00111920027

Office of Naval Research (ONR) award (N00014-19-1- 2134)


Email Address of Submitting Author


University of Toronto



ORCID For Submitting Author


Declaration of Conflict of Interest

A.A.-G. is a co-founder and the Chief Visionary Officer of Kebotix, Inc.

Version Notes

Pre-submission, version 1.