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.
hub
Jacobsen , year =
7 Pith papers cite this work, alongside 1,247 external citations. Polarity classification is still indexing.
7
Pith papers citing it
1,247
external citations · Crossref
hub tools
citation-role summary
method 1
citation-polarity summary
years
2026 7roles
method 1polarities
use method 1representative 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.
Develops a Laguerre spectral minimum action method with time rescaling and improved quadrature for efficient quasi-potential computation in infinite-horizon problems.
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.
An open-source Snakemake workflow fully automates NEB reaction path calculations with ML potentials and recovers the known HCN-HNC energy profile without manual steps.
NO2 adsorption on alpha-Fe2O3 transfers 0.72 electrons and quenches surface small polarons, suppressing polaronic conductivity and explaining sensor resistance increase.
Bayesian optimization with Gaussian processes unifies minimization, single-point saddle searches, and double-ended path searches on potential energy surfaces through a shared six-step surrogate loop using derivative observations and inverse-distance kernels.
citing papers explorer
-
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.
-
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.
-
An Efficient Laguerre Minimum Action Method for Computing Quasi-Potentials
Develops a Laguerre spectral minimum action method with time rescaling and improved quadrature for efficient quasi-potential computation in infinite-horizon problems.
-
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.
-
Reproducible Orchestration of Best Practices for Reaction Path Optimization with the Nudged Elastic Band
An open-source Snakemake workflow fully automates NEB reaction path calculations with ML potentials and recovers the known HCN-HNC energy profile without manual steps.
-
Polaron Conductivity in $\alpha$-Fe2O3 Quenched by Adsorbed NO2
NO2 adsorption on alpha-Fe2O3 transfers 0.72 electrons and quenches surface small polarons, suppressing polaronic conductivity and explaining sensor resistance increase.
-
A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches
Bayesian optimization with Gaussian processes unifies minimization, single-point saddle searches, and double-ended path searches on potential energy surfaces through a shared six-step surrogate loop using derivative observations and inverse-distance kernels.