AI teaches itself to identify materials – and predict new ones too

A deep learning neural network trained on 50,000 crystal structures of inorganic materials has acquired the ability to recognise chemical similarities and predict new materials. One way to find out whether two elements from the periodic table will form a crystalline material is the tried and trusted'shake and bake' – mix them together at a range of different stoichiometries and hope for the best. Binary materials are thus very well covered in the scientific literature, but this method can't keep up with the vastly more complex combinatorial possibilities afforded by three or more elements. Therefore, predictions of which elements will combine in which ratios to form regular solids are necessary and they hold the promise of new materials with desirable or even unprecedented properties. Current prediction methods typically use evolutionary algorithms and apply them to random starting structures.