In the future, we intend to extend this approach to more materials databases. Net detection and topology identification are easily automated, but the main limiting step at this moment in our methodology is the detecting of bonding. For inorganic structures, the Crys-talNN algorithm has good accuracy, but it is not well adapted and has important limitations for organic or organic-containing compounds, or intermetallic structures. We have developed heuristics for subclasses of crystals, such as metal-organic frameworks, allowing us to screen MOF databases such as CoRE MOF. 11 However, no truly general solution for accurate bond detection is available to our knowledge. Further development in this direction is needed, and database could also, in the future, include bonding information -as does the recently released MOSAEC-DB. This will allow, in turn, to search for MOFs with specific properties linked to their topology, such as defect-tolerant MOFs with good mechanical stability or acting as mechanical meta-materials. Another promising avenue of research is the use of more complex decomposition strategies to determine topology in specific families of materials. For example, in ionic compounds such as oxides, sulfides, or halides, one could choose a representation where only cations are included as vertices, and bridging anions are transformed into edges.