One approach to further quantifying the synthesiability of a new compound is to build statistical models based on data of known compounds. This can be treated as classification (yes/no) problem or as regression problem based on probability of success. While there is positive labelled data, i.e. the set of known materials, negative data on 'unsynthesisable' materials is generally unavailable. There has been successes in using information from failed experiments, 77 but probabilistic models are more generally built from positive and unlabelled data for specific classes of material. For more universal models, a large amount of diverse data is required. There has been progress in text mining of synthesis information from the literature. Kononova et al. extracted 19,488 recipes for inorganic crystals including a breakdown into precursors, operations, and processing conditions. Such data can then be used to make predictions of optimal crystal synthesis procedures. Even if the synthesis of a specific metastable material is possible, a related question is its lifetime before transformation into a lower energy equilibrium configuration. The answer requires knowledge of the free energy landscape and the corresponding barriers and kinetics of the system. For molecules and nanoparticles, this is relatively straightforward to explore computationally and a range of techniques are available. However, for extended crystals modelled within periodic boundary conditions it requires prior knowledge of the pathways between two or more structures. For crystals connected by group-subgroup relations this can be straightforward (e.g. from a cubic to tetragonal perovskite ), and the methods from anharmonic phonon theory can be useful to establish the (local) stability range of high temperature phases. However, there are many transitions where no smooth pathway is accessible (e.g. incommensurate polymorphs ).