Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
Uncertainty-driven dynamics for active learning of interatomic potentials
3 Pith papers cite this work, alongside 124 external citations. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
Transition path sampling serves as an active learning engine to build machine-learned potentials accurate in barrier regions, enabling discovery of multiple protonation mechanisms in CO2 reduction on copper.
citing papers explorer
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Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
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Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
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Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials
Transition path sampling serves as an active learning engine to build machine-learned potentials accurate in barrier regions, enabling discovery of multiple protonation mechanisms in CO2 reduction on copper.