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P. Ananth

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#machine learning Preprint Aug 2026

AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials

AdaptNTK is introduced, a single-model framework that measures uncertainty as a regularized Mahalanobis distance in empirical neural tangent kernel (NTK) feature space, providing efficient single-model uncertainty estimation with sequential updates for data-efficient active learning.

P. Ananth, Shuwen Yue · 0 citations