Building informative materials datasets beyond targeted objectives
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918486313271296 |
|---|---|
| author | Castañeda, Rafael Espinosa Dale, Ashley Wang, Hongchen Kurniawan, Yonatan Wan, Hao Zhang, Runze Dieng, Adji Bousso Li, Kangming Hattrick-Simpers, Jason |
| author_facet | Castañeda, Rafael Espinosa Dale, Ashley Wang, Hongchen Kurniawan, Yonatan Wan, Hao Zhang, Runze Dieng, Adji Bousso Li, Kangming Hattrick-Simpers, Jason |
| contents | 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 without our diversity-aware framework, prediction performance on untargeted properties can degrade by up to 40% relative to random sampling, whereas applying our framework yields improvements of up to 10% . For targeted properties, performance can degrade with respect to random sampling by up to 12.5% without diversity, while our framework achieves gains of up to 25%. Incorporating diversity into dataset construction not only preserves informativeness for the targeted properties, but also improves materials coverage for potential future objectives. As a result, the constructed datasets remain broadly informative across considered and unconsidered outcomes, ensuring unbiased quality entries and mitigating cold-start limitations in subsequent modeling and discovery campaigns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05104 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Building informative materials datasets beyond targeted objectives Castañeda, Rafael Espinosa Dale, Ashley Wang, Hongchen Kurniawan, Yonatan Wan, Hao Zhang, Runze Dieng, Adji Bousso Li, Kangming Hattrick-Simpers, Jason Materials Science Artificial Intelligence Databases Machine Learning Applications 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 without our diversity-aware framework, prediction performance on untargeted properties can degrade by up to 40% relative to random sampling, whereas applying our framework yields improvements of up to 10% . For targeted properties, performance can degrade with respect to random sampling by up to 12.5% without diversity, while our framework achieves gains of up to 25%. Incorporating diversity into dataset construction not only preserves informativeness for the targeted properties, but also improves materials coverage for potential future objectives. As a result, the constructed datasets remain broadly informative across considered and unconsidered outcomes, ensuring unbiased quality entries and mitigating cold-start limitations in subsequent modeling and discovery campaigns. |
| title | Building informative materials datasets beyond targeted objectives |
| topic | Materials Science Artificial Intelligence Databases Machine Learning Applications |
| url | https://arxiv.org/abs/2605.05104 |