自回归玻尔兹曼生成器
Autoregressive Boltzmann Generators
AI总结：
提出自回归玻尔兹曼生成器（ArBG），通过自回归建模替代基于流的模型，克服拓扑约束并提升可扩展性，在分子系统采样中显著优于现有方法，并在8残基系统上将零样本能量误差降低60%以上。
摘要：
在热力学平衡下高效采样分子系统是统计物理学中的一个标志性挑战。这一挑战推动了玻尔兹曼生成器（BGs）的发展，它通过结合具有精确似然性的生成模型和重要性采样校正，能够快速生成不相关的平衡样本。然而，现代BGs主要依赖归一化流（NFs），后者要么由于严格的可逆性约束（离散时间）而表达性有限，要么由于计算昂贵的似然性（连续时间）而受限。在本文中，我们提出自回归玻尔兹曼生成器（ArBG）——一种新颖的自回归建模框架——通过脱离基于流的BG范式来克服这些限制。ArBG规避了流的拓扑约束，支持顺序推理时干预，同时通过利用在大型语言模型中有效的架构提供了增强的可扩展性。我们通过实验证明，ArBG在所有基准测试中均显著优于基于流的模型，特别是在较大的肽系统（如10残基的Chignolin）中。此外，我们引入了Robin，一个1.32亿参数的可迁移模型，该模型使用ArBG框架训练，改进了先前的最先进技术，将8残基系统上的零样本能量误差E-W$_2$降低了60%以上。代码可在以下链接找到：this https URL。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
