Building informative materials datasets beyond targeted objectives
Materials science data collection can be expensive, making the reuse and long-term utility of datasets critical important for future discovery campaigns. In practice, researchers prioritize a subset of properties due to research interests. However, ignoring a subset of outcomes in data collection campaigns potentially generate datasets poorly suited for future learning tasks. Here, we present a framework for dataset construction that maximizes informativeness for target properties of interest while preserving performance on untargeted ones. Our approach uses diversity-aware selection to ensure broad coverage of the materials space. In noisy experimental dataset construction, we find that ...
| Authors | Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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