Let Materials Science Data Learn to Reason

Materials science excels at collecting numbers but often loses sight of the conditions, mechanisms, and provenance that give those numbers meaning. We have vast databases, long papers, and a forest of plots, yet the bench-top questions persist: what works here, now, under these conditions and why? This Viewpoint argues for a practical posture: treat conditions as first principles, make mechanisms explicit, and attach sources and uncertainty to every claim. We outline a plain work loop, i.e., ask → retrieve → compute → validate → write back, that increases learning speed without requiring “hero” datasets or black-box models. We demonstrate how these habits are applied in practice across catalysis, solid-state batteries, and hydrogen storage, and we highlight platform design choices (e.g., DigMat Platform) that enforce them. The goal is not fireworks, but fewer wrong turns: fairer comparisons, predictions tied to the exact world they apply to, and decisions that withstand daylight.