How the Shape of Chemical Data Can Enable Data-Driven Materials Discovery

Abstract

Chemical data have been created from many different origins. The chemicals themselves tend to be synthesized out of curiosity or as an industry-led need. Their materials characterization and development for functional applications generate cognate data about their structures and properties. Chemical structures and properties may also be computed ahead of their physical creation. The collation of all this chemical information affords a ‘chemical space’ that encapsulates a rich and diverse set of data. This opinion article considers the shape and size of this chemical space and of its various subdomains, how the relative availability of its structure and property information governs what type of questions one should ask of the data, and what type of machine learning (ML) should be applied to discover a new material. Application examples of ML methods that produce predictive models for data-driven materials discovery are discussed.