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.
and De, Sandip and Poelking, Carl and Bernstein, Noam and Kermode, James R
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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.
Electron dynamics in the aromatic pi cloud quench and amplify specific vibrational signals from a single water molecule bound to pyrene anion.
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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Electron dynamics mediate the water-carbon {\pi} bond
Electron dynamics in the aromatic pi cloud quench and amplify specific vibrational signals from a single water molecule bound to pyrene anion.