It is tempting to believe that bigger models automatically produce better science. In practice, the quality of training data dominates nearly every benchmark in computational chemistry.
Experimental datasets contain inconsistencies, missing metadata, and hidden biases. Systematic cleaning, standardization of units and nomenclature, and documented provenance are what separate reliable benchmarks from noise.
That is why ChemAiX invests heavily in dataset curation pipelines before a single model is trained, and why we publish clear documentation about the data behind every AI-assisted result.