Table of Contents

Volume 533 Number 7601 pp7-138

5 May 2016

About the cover

Many chemical reactions never make it into the methods sections of journal articles because they are deemed ‘unsuccessful’. However, such reactions still provide valuable information about the bounds on reaction conditions needed for product formation. Alexander Norquist and colleagues have taken a set of such ‘dark reactions’ from laboratory notebooks — specifically for the formation of templated vanadium selenites — and enriched the data with chemical information and properties. They then used a machine-learning algorithm trained on the dark reactions dataset to predict reaction outcomes. The algorithm was able to predict reaction successes or failures with greater accuracy than human intuition. This demonstrates both the value of wider dissemination of unsuccessful syntheses, and the possibility of using machine learning to arrive at potential synthetic routes faster than by traditional means. Cover: Nik Spencer/Nature

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