arXiv Machine Learning
Inclusive electron-nucleus cross section models from domain adaptation
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arXiv Machine Learning
Jun 8
Machine Learning for Electron-Scale Turbulence Modeling in W7-X
arXiv:2511. 04567v2 Announce Type: replace-cross Abstract: Constructing reduced models for turbulent transport is essential for accelerating profile predictions and enabling many-query tasks such as parameter exploration and design optimization.
arXiv Machine Learning
Jul 17
Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields
arXiv:2607. 14486v1 Announce Type: cross Abstract: Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows.
arXiv Machine Learning
Sep 10
A budget-dependent crossover between coverage- and response-based training-set selection for machine-learned interatomic potentials
arXiv:2609.05877v1 Announce Type: new Abstract: Selecting compact training sets for machine-learned interatomic potentials requires deciding whether to preserve structural diversity or target configu...
arXiv Machine Learning
Aug 5
ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density
arXiv:2608. 03260v1 Announce Type: new Abstract: Pretraining has shown strong potential for learning transferable representations, yet it remains underexplored for electron-density-based molecular learning.
arXiv Machine Learning
Sep 22
Physics-residual machine learning predicts oxygen-evolution catalyst activity beyond the training range from sparse polarization measurements
arXiv:2609.23549v1 Announce Type: new Abstract: Screening oxygen-evolution catalysts on combinatorial libraries requires deciding which candidates receive the remaining measurements. The deciding act...
arXiv Machine Learning
Sep 25
The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning
The paper investigates the effectiveness of Δ-learning in scientific machine learning, showing that simply reducing residual error magnitude does not guarantee easier learning. By testing molecular graph neural networks on total energy predictions, the authors find that complex local descriptor baselines can produce small residuals that are actually rougher and harder to learn, whereas a semi‑empirical baseline both shrinks the residual scale and smooths the target space. They propose a new diagnostic, scale‑normalized graph Dirichlet roughness (SD_{R}), to assess residual learnability and argue that choosing complementary baselines is as important as model architecture for successful target design.