Generative Nested Sampling of Atomistic Thermodynamic Landscapes
Read the original on arXiv Machine Learning →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.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.