arXiv Statistics ML By Gabriele Bandini, Giulio Biroli, Patrick Charbonneau, Andrea Gambassi

Overcoming critical slowing down in frustrated spin systems by learned multiscale sampling

Read the original on arXiv Statistics ML →

The Flow has not summarised this story yet — read it at arXiv Statistics ML.

arXiv Machine Learning
Sep 15

Quenched Ensemble Sampling

arXiv:2609.15894v1 Announce Type: cross Abstract: Some of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states cha...

By David Yallup
arXiv Machine Learning
Sep 4

Generative Nested Sampling of Atomistic Thermodynamic Landscapes

The paper introduces NS‑Flows, a flow‑based nested sampling method that replaces Markov‑chain updates with a conditional normalizing flow trained on live sets. By applying this technique to a Lennard‑Jones particle system, the authors achieve over two orders of magnitude fewer energy evaluations and a roughly one‑third reduction in wall‑clock time compared to traditional nested sampling. The study also shows that the flow’s generation efficiency varies non‑monotonically along the annealing trajectory, providing a diagnostic of the system’s internal mode complexity and identifying liquid‑like ensembles as the most challenging for current flow architectures.

By Alessandro Coretti, Nico Unglert, Sebastian Falkner, Georg K. H. Madsen, Christoph Dellago