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AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials
Machine learning interatomic potentials bridge the gap between quantum chemical precision and classical computational speed, enabling molecular dynamics simulations with first-principles accuracy. Their reliability is often improved through active learning, which iteratively expands the training set
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