The effect of the host structure's geometry has been less studied, though it has a significant effect on the thermodynamic properties computed in GCMC simulations. To overcome these limitations, one generally proceeds in two steps: first, screening all structures of a database for a targeted properties using generic FFs and clean experimental structures; secondly, refining the ranking after FFs and geometry optimization on the best-performing materials. Another approach is to rank materials in a screening study for a given application and varying the FFs, then assigning uncertainty on the outcomes related to the underlying model. Since numerous screening studies rely on shared assumptions and database structures, the community would benefit from disseminating reference data derived from simulations. This would improve computational reproducibility and facilitate the evaluation of accuracy for training datasets, particularly in the context of recent machine learning models that leverage GCMC results for fast predictions of MOF properties.