Chemical rules and concepts are traditionally based on modest amounts of experimental and computational data The underlying premise is that these concepts will prove more widely applicable than merely in the context from which they were originally derived The question must be asked whether in this way no important concepts are missed

In recent years, there have been important developments in the fields of ‘ig data’and ‘achine learning’ aimed at making large amounts of data manageable One of these developments is the inception of ‘eep neural networks’These networks can automatically detect patterns and concepts when given large amounts of data Until recently, neural networks were essentially black boxes, in which the detected patterns remained hidden However, thanks to advances in the field of ‘xplainable artificial intelligence’ extracting these patterns in human intelligible terms has become feasible

In this project, we will investigate if we can discover chemical rules and concepts by only using computers: first, to calculate enormous amounts of chemical data, and second, to (re)discover and extend chemical concepts from this data using deep neural networks By tackling incrementally more difficult problems, we aim to gradually promote the computer from a number cruncher to an insightful research assistant