Active Learning Strategies Show Varied Performance in Materials Science AI Applications, Study Finds
A comprehensive study evaluating active learning strategies combined with automated machine learning reveals substantial performance variations in materials science applications. Researchers found that strategies incorporating model gradient information outperformed traditional approaches, while model-free methods consistently underperformed across multiple datasets.
Benchmarking Active Learning in Materials Science
Researchers have conducted a comprehensive evaluation of active learning strategies combined with automated machine learning for small-sample regression tasks in materials science, according to recent reports published in Scientific Reports. The study systematically compared 18 distinct AL strategies across 14 single-output regression tasks derived from 9 materials datasets, providing new insights into optimal approaches for data-efficient machine learning in scientific applications.
