Paper

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials

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arXiv:2606. 04100v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data.

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Authors: Joanna Zou, Fraser Birks, Dallas Foster, Youssef Marzouk
Released: 2026-06-05 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: line, rate, compare, symmetry, lora, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.04100

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