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.
- Open Access
- Review
Let Materials Science Data Learn to Reason
Author Information
Received: 06 Oct 2025 | Revised: 07 Dec 2025 | Accepted: 17 Dec 2025 | Published: 22 Dec 2025
Abstract
Keywords
AI for Materials | condition-first reasoning | data provenance | mechanism-constrained inference | out-of-distribution
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