Andrij Vasylenko1,Jacinthe Gamon1,Benjamin Duff1,Vladimir Gusev1,Luke Daniels1,Marko Zanella1,Felix Shin1,Paul Sharp1,Alexandra Morscher1,Ruiyong Chen1,Alex Neal1,Laurence Hardwick1,John Claridge1,Frederic Blanc1,Michael Gaultois1,Matthew Dyer1,Matthew Rosseinsky1
University of Liverpool1
Andrij Vasylenko1,Jacinthe Gamon1,Benjamin Duff1,Vladimir Gusev1,Luke Daniels1,Marko Zanella1,Felix Shin1,Paul Sharp1,Alexandra Morscher1,Ruiyong Chen1,Alex Neal1,Laurence Hardwick1,John Claridge1,Frederic Blanc1,Michael Gaultois1,Matthew Dyer1,Matthew Rosseinsky1
University of Liverpool1
The selection of the elements to combine delimits the possible outcomes of synthetic chemistry because it determines the range of compositions and structures, and thus properties, that can arise. For example, in the solid state, the elemental components of a phase field will determine the likelihood of finding a new crystalline material. Researchers make these choices based on their understanding of chemical structure and bonding. Extensive data are available on those element combinations that produce synthetically isolable materials, but it is difficult to assimilate the scale of this information to guide selection from the diversity of potential new chemistries. Here we show that unsupervised machine learning captures the complex patterns of similarity between element combinations that afford reported crystalline inorganic materials. This model guides prioritisation of quaternary phase fields containing two anions for synthetic exploration to identify lithium solid electrolytes in a collaborative workflow that leads to the discovery of Li<sub>3.3</sub>SnS<sub>3.3</sub>Cl<sub>0.7.</sub>