Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
Machine learning speeds up synthesis of porous materials
Failed chemical reactions are often not reported, which means that vast amounts of potentially useful data are going to waste. Experiments show that machine learning can use such data to optimize the preparation of porous materials.
Intuition often guides our choices throughout life. In science, it can also play a part in the design of experiments to answer or probe a question of interest. For example, it guides chemists to select a specific set of reagents, reactions or conditions when devising the synthesis of a target compound. Writing in Nature Communications, Moosavi et al.1 report their use of machine learning to capture this sort of intuition to optimize the synthesis of an emerging class of material known as metal–organic frameworks (MOFs), which have applications as diverse as fuel storage, catalysis and the capture of water from the atmosphere.