Currently, the growing amount of structural and simulation data is generating a lot of enthusiasm for creating machine learning models capable of predicting the properties of new materials. Over the last few years, a series of classes of ML models (simple ML models for Although the accuracy of machine learning models might keep increasing in the coming years, the quality of training data intrinsically limits the accuracy of models for applications. Indeed, most ML studies assume that materials properties are fully encoded in their crystalline atomic-scale structures. However, porous materials experimentally have physical and chemical properties that depend on other factors, such as synthesis conditions, purification steps and material formulation, textural properties, presence of defects, etc. Model validation steps need to be carried out directly on high-quality experimental data sets, which can be guaranteed by the use of FAIR data processing tools and standardized best practices. Some studies have already established prediction-guided protocols based on powerful language models using mined synthesis data. However, all data can not be mined with the same degree of accuracy when metadata are missing or non-standardized measurements are used. As an alternative, porous materials discovery projects could combine both experimental and modeling approaches to build up consistent datasets. Automation of the synthesis and characterization steps with active learning feedback and including negative results will open up new perspectives in this direction, however it is still in the very early stages of its development. of Metal-Organic Frameworks by a Robotic Platform and Bayesian Optimization. ACS Appl. Mater. Interfaces 2021, 13, 53485-53491.