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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5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5representative citing papers
Causal analysis of water MD simulations shows translational motions drive orientational dynamics in supercooled HDL but remain decoupled at ambient conditions, revealing an emergent arrow of time in fluctuation couplings.
A novel neural architecture based on Pairformer is introduced for learning committor functions to better capture dynamical features in biomolecular rare events without specialized priors.
Machine-learned many-body potentials from Poisson-Boltzmann calculations on clusters up to 48 colloids show that higher-order interactions reduce cohesion and eliminate broad gas-liquid phase separation, consistent with primitive model pair and triplet potentials.
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.
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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Causality in Liquid Water as a Hallmark of Emergent Glassy Dynamics
Causal analysis of water MD simulations shows translational motions drive orientational dynamics in supercooled HDL but remain decoupled at ambient conditions, revealing an emergent arrow of time in fluctuation couplings.
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Navigating committor landscape of biomolecules with a general pairwise interaction model
A novel neural architecture based on Pairformer is introduced for learning committor functions to better capture dynamical features in biomolecular rare events without specialized priors.
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Many-body attractions do not stabilize gas-liquid phase separation in aqueous dispersions of charged colloids within the Poisson-Boltzmann framework
Machine-learned many-body potentials from Poisson-Boltzmann calculations on clusters up to 48 colloids show that higher-order interactions reduce cohesion and eliminate broad gas-liquid phase separation, consistent with primitive model pair and triplet potentials.
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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.