Reticular chemistry, the science of constructing extended crystalline structures from molecular building blocks, gives scientists a unique playground for material design and discovery as it gives access to a practically infinite-dimensional design space across many length scales: One can architect the pore, functionalize the building blocks, or even encode chemical sequences across unit cells. It is not possible to explore this entire design space by mere trial-and-error using brute-force computational screenings and iterative experimental testing. Similar to many other scientific domains, this realization gave rise to the notion of digital reticular chemistry, and in particular to the use of data-intensive research to aid the discovery and design of new reticular materials for any given application by learning predictive models from data. Thus far, machine-learning approaches have-amongst others-been used to predict gas adsorption properties, colors, oxidation states, electronic properties, heat-capacities, or synthesis conditions 14 as well as (water) stability of metal-organic frameworks (MOFs). This work is motivated by the observation that the full potential of data science in this field