Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives

arXiv:2605.05104v2 Announce Type: replace-cross Abstract: Materials science data collection can be expensive, making dataset reuse and long-term utility important for future discovery. In practice, data collection often prioritizes properties of current interest while other properties are excluded from the acquisition process. This can produce datasets that are well suited for current objectives but lose information needed for properties of future interest. Here, we present how to construct datasets that remain informative for both. We develop and evaluate acquisition frameworks that combine predictive disagreement with diversity in material features, predicted outcomes, or both. We test these approaches across four DFT datasets and an experimental thermoelectric dataset. Diversity inclusion in dataset construction can reduce present and future objectives trade-off by preserving a broader range of materials and property behavior during acquisition. Across all datasets, the Non-dominated Sorting Genetic Algorithm II with disagreements and feature diversity (NSGA-II Dis. + diversity) framework developed in this work provides the most robust balance between current and future objectives. It maintains prediction performance above random sampling for current objectives while mitigating bias on excluded properties and, in many cases, improving their performance beyond random sampling. This diversity-aware acquisition framework provides a practical route to building materials datasets with unbiased quality entries and mitigating cold-start limitations in subsequent modeling and discovery campaigns.

Abstract:Materials science data collection can be expensive, making dataset reuse and long-term utility important for future discovery. In practice, data collection often prioritizes properties of current interest while other properties are excluded from the acquisition process. This can produce datasets that are well suited for current objectives but lose information needed for properties of future interest. Here, we present how to construct datasets that remain informative for both. We develop and evaluate acquisition frameworks that combine predictive disagreement with diversity in material features, predicted outcomes, or both. We test these approaches across four DFT datasets and an experimental thermoelectric dataset. Diversity inclusion in dataset construction can reduce present and future objectives trade-off by preserving a broader range of materials and property behavior during acquisition. Across all datasets, the Non-dominated Sorting Genetic Algorithm II with disagreements and feature diversity (NSGA-II Dis. + diversity) framework developed in this work provides the most robust balance between current and future objectives. It maintains prediction performance above random sampling for current objectives while mitigating bias on excluded properties and, in many cases, improving their performance beyond random sampling. This diversity-aware acquisition framework provides a practical route to building materials datasets with unbiased quality entries and mitigating cold-start limitations in subsequent modeling and discovery campaigns.

Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Databases (cs.DB); Machine Learning (cs.LG); Applications (stat.AP)

arXiv:2605.05104 [cond-mat.mtrl-sci]

(or arXiv:2605.05104v2 [cond-mat.mtrl-sci] for this version)

https://doi.org/10.48550/arXiv.2605.05104 Focus to learn more arXiv-issued DOI via DataCite

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Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives

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