The theory and simulation of molecules and materials has become increasingly accurate and predictive over the past few decades. The process of computing the chemical and physical properties of a known compound is now well established. The next challenge is to explore the vast space of unknown compounds, and to identify materials with the properties required to support the next-generation of technologies. This is being supported by rapid developments in both hardware (classical supercomputers and the first quantum computers) and software (new algorithms and statistical approaches). Transfer of knowledge from the artificial intelligence community has the potential to supercharge chemical discovery by accessing a large phase space of potential compounds that is inaccessible by high throughput experiments or traditional calculations alone [1,2].
After providing a snapshot of the current status and future direction in this field, I will illustrate developments using our recent progress in the exploration of hybrid organic-inorganic frameworks, where the interplay two distinct chemical building blocks can result in emergent behaviour. This includes the first report of a metallic metal-organic framework (MOF) , engineering redox-activity in the organic ligands and inorganic clusters , as well as applications to solar energy conversion in the form of hybrid halide perovskites .
 “Computational screening of all stoichiometric inorganic materials”, Chem 1, 617 (2016).
 “Machine learning for molecular and materials science”, Nature 559, 547 (2018).
 “Metallic conductivity in a 2D cobalt dithiolene metal–organic framework”, J. Am. Chem. Soc. 31, 10863 (2017).
 “Redox-active metal–organic frameworks for energy conversion and storage”, J. Mater. Chem. A 7, 16571 (2019).
 “Dielectric and ferroic properties of metal halide perovskites”, APL Materials 7, 010901 (2019).