Krishnaswamy Harish

Objective-Dependent Active Learning and Calibrated Uncertainty for Sample-Efficient Discovery of Rare-Earth Extractants: A Benchmark on 1,202 Experimental Distribution Coefficients with Prospective Validation

Measuring distribution coefficients (log D) for rare-earth solvent extraction is slow and expensive, making sample-efficient machine learning attractive. However, the practical questions of which acquisition strategy to use, whether the attendant uncertainty estimates can be trusted, and how well a model actually gener...