Abstract
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(7)Si(2)S(7)I illustrates the role of the workflow in exploring design hypotheses constructed by synthesis researchers and the role of new materials in increasing understanding, in this case by expanding the design paths available for superionic transport. Substitution into Li(7)Si(2)S(7)I affords a structurally related material with superior low temperature transport properties, emphasizing the role of new structures in enabling subsequent materials optimization by compositional modification founded on that structural scaffold.We contrast our focused hypothesis-driven approach with the recent screening studies that cover a much broader range of chemistries and do not target novel structural motifs. These approaches are good at interpolation and identifying the low hanging fruit for substitutional chemistry, but they struggle to deliver new chemistry knowledge, new understanding and new experimentally observed crystal structures. We comment on reporting the large number of proposed hypothetical structures when considering advances in prediction and the importance of context of the size of the chemical space including continuous composition variation and disorder. An example is the difference between predicting superstructures of known parent structures and experimentally realizing these in the face of competition from structural disorder. Given the scope for prediction of candidates, discussion of structural novelty can usefully be restricted to realized experimental examples based on expert interrogation of their structures. We advocate for bringing experts from chemistry and computer science together to design hypothesis-based routes to materials discovery that incorporate appropriate assessment of novelty.