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Sample-efficient active learning for materials informatics using integrated posterior variance
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DOI：10.1016/j.commatsci.2026.114551.png)
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Abstract
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• Active learning - Compares Integrated Posterior Variance (IPV) against random sampling, point-wise uncertainty sampling, and query-by-committee. • Multi-dataset evaluation - Benchmarked on three diverse datasets; AutoAM, Thermoelectric, and NMR. • Performance gain - IPV consistently selects candidates that reduce prediction error with fewer labeled samples than other selection strategies. • Limitations - (1) Computational overhead grows with dataset size; (2) Effectiveness declines in high-dimensional spaces where distance metrics degrade. • Practical impact - Enables strategic experiment selection that cuts required experiments for materials discovery workflows.
Keywords:
Machine learning
Artificial intelligence
Active learning
Materials science
Uncertainty
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