Discovery of Crystalline Inorganic Solids in the Digital Age.

ConspectusThis Account considers how the discovery of crystalline inorganic materials, defined as their experimental realization in the laboratory, can benefit from computation: computational predictions afford candidates for laboratory exploration, not discoveries themselves. The discussion distinguishes between the novelty of a material in terms of its composition and in terms of its structure. The stepwise modification of the composition of a parent material with retention of its crystal structure can reduce the risk in seeking new materials and offers the ability to fine-tune properties which has demonstrated value in optimizing materials performance. However, the parent structures first need to be identified, thus emphasizing the importance of materials discovery beyond simple analogy as a key complementary activity. We describe a workflow we have developed to accelerate discovery of such new structures by addressing many of the challenges, in particular the identification of chemistries that are likely to afford materials and the targeting of reactions within their compositional spaces. Data on experimentally isolated phases are used to prioritise candidate chemistries with machine learning, and crystal structure prediction is used to target compositions within those chemistries for synthesis by computationally constructing probe structures whose energies are indicative of the accessible stability at a given composition. We show how this workflow usefully identifies the parts of chemical space offering new materials and has afforded new structures in practice. The discovery of the solid lithium electrolyte Li