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cs.LG2025
Training-Free Active Learning Framework in Materials Science with Large Language Models
Hongchen Wang, Rafael Espinosa Castañeda, Jay R. Werber +3
Active learning (AL) accelerates scientific discovery by prioritizing the most informative experiments, but traditional machine learning (ML) models used in AL suffer from cold-sta…
cs.LG2025
Exploring the Limitations of kNN Noisy Feature Detection and Recovery for Self-Driving Labs
Qiuyu Shi, Kangming Li, Yao Fehlis +4
Self-driving laboratories (SDLs) have shown promise to accelerate materials discovery by integrating machine learning with automated experimental platforms. However, errors in the…