想了解分子模拟新方法？这篇论文提出的 ArBG 用自回归替代流模型，在 Chignolin 等体系上效果更好，还开源了 1.32 亿参数的 Robin 模型。
论文提出 Autoregressive Boltzmann Generators (ArBG)，一种自回归建模框架，用于分子系统平衡采样，克服了基于流的 Boltzmann Generators 的表达性限制。ArBG 在所有基准测试上优于流模型，特别是在 10-残基 Chignolin 肽系统上。作者还发布了 Robin，一个 1.32 亿参数的可迁移模型，在 8-残基系统上将零样本能量误差 E-W₂ 降低超过 60%。代码已开源在 GitHub。
Autoregressive Boltzmann Generators
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann Generators (BGs), which allow rapid generation of uncorrelated equilibrium samples by combining a generative model with exact likelihoods and an importance sampling correction. However, modern BGs predominantly rely on normalizing flows (NFs), which either suffer from limited expressivity due to strict invertibility constraints (discrete time) or computationally expensive likelihoods (continuous time). In this paper, we propose Autoregressive Boltzmann Generators (ArBG) -- a novel autoregressive modelling framework -- that overcomes these limitations by departing from the flow-based BG paradigm. ArBG circumvents the topological constraints of flows and enables sequential inference-time interventions, while offering enhanced scalability by leveraging architectures effective in Large Language Models. We empirically demonstrate that ArBG leads to significant improvements over flow-based models across all benchmarks, but particularly in larger peptide systems such as the 10-residue Chignolin. Furthermore, we introduce Robin, a 132 million parameter transferable model trained with the ArBG framework which improves over the previous state-of-the-art, reducing the zero-shot energy error, E-W$_2$, on 8-residue systems by over 60$\%$. The code can be found at the following link: https://github.com/danyalrehman/autobg.