Robotic chemistry lab joins forces with Google AI to predict then make new inorganic materials

Artificial intelligence (AI) tools, trained on the scientific literature, have been tasked with discovering and synthesising inorganic compounds. The researchers hope that the AI, which was combined with an automated chemistry platform, can be used to speed up the discovery of novel materials such as recyclable plastics and transparent conductors for cheaper solar panels.

Discovering energetically favourable inorganic crystals is a fundamental focus of solid-state chemistry, but identifying promising new materials without automation can be challenging and time-consuming.

In the first of two papers on the topic, researchers at Google DeepMind developed a deep learning tool called Graph Networks for Materials Exploration (Gnome), which they trained on a large and diverse data set developed by the Materials Project – an open-access database created by the Department of Energy’s Lawrence Berkeley National Laboratory in 2011 – and used it to filter candidate structures.¹