Additionally, we find that the vast majority of the synthetic products were wrongly characterized. These misinterpreted characterizations broadly fall under two categories: either authors failed to recognize that the automated refinement process changed the symmetry of the target compound, or the PXRD pattern agrees better with known, or more often a mixture of known phases. A more detailed explanation of these pitfalls will be laid out in a following section. In general, it seems that one issue lies with the final characterization step (in this case the Rietveld refinement), thus improvement of AI-assisted materials characterization seems to be one the bottlenecks of automated materials discovery. Another might be related to the role of disorder in materials and how this is often not modeled or considered when new materials are predicted. Thus materials prediction could also be improved by considering the role of disorder.