The value of negative results in data-driven catalysis research

Data science and machine learning have the potential to accelerate the discovery of effective catalysts; however, these approaches are currently held back by the issue of negative results. This Comment highlights the value of negative data by assessing the bottlenecks in data-driven catalysis research and presents a vision for a way forwards.

Automated summary

Data science and machine learning have the potential to greatly improve catalyst discovery. However, the issue of negative results is currently hindering progress. This Comment highlights the value of negative data and proposes a vision for overcoming the challenges in data-driven catalysis research.