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arxiv 2404.16566 v1 pith:RA6QOBJY submitted 2024-04-25 physics.comp-ph

Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems

classification physics.comp-ph
keywords boltzmanndifferentgeneratorsmany-bodysamplingstatessystemsbeyond
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The paper by No\'e et al. [F. No\'e, S. Olsson, J. K\"ohler and H. Wu, Science, 365:6457 (2019)] introduced the concept of Boltzmann Generators (BGs), a deep generative model that can produce unbiased independent samples of many-body systems. They can generate equilibrium configurations from different metastable states, compute relative stabilities between different structures of proteins or other organic molecules, and discover new states. In this commentary, we motivate the necessity for a new generation of sampling methods beyond molecular dynamics, explain the methodology, and give our perspective on the future role of BGs.

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  1. Jeffreys Flow: Robust Boltzmann Generators for Rare Event Sampling via Parallel Tempering Distillation

    cs.LG 2026-04 unverdicted novelty 6.0

    Jeffreys Flow distills Parallel Tempering trajectories via Jeffreys divergence to produce robust Boltzmann generators that suppress mode collapse and correct sampling inaccuracies for rare event sampling.