In lieu of a traditional "outlook" section, we want to highlight here the recent and important impact of novel methodologies for materials discovery based on large amounts of data, relying on the development of high-throughput screening procedures and their combination with machine learning algorithms. Such data-based workflows represent a recent and important evolution in the field of materials discovery, as well as in other areas of chemistry and physical sciences more broadly. In this section, we explore some of the possibilities raised by these data-based approaches for the task of identifying (or discovering) novel framework materials with targeted stimuli-responsive behavior.