The generation of equilibrium samples of molecular systems has been a long-standing problem in statistical physics. Boltzmann Generators are a generative machine learning method that addresses this issue by learning a transformation via a normalizing flow from a simple prior distribution to the target Boltzmann distribution of interest. Recently, flow matching has been employed to train Boltzmann Generators for small molecular systems in Cartesian coordinates. We extend this work and propose a first framework for Boltzmann Generators that are transferable across chemical space, such that they predict zero-shot Boltzmann distributions for test molecules without being retrained for these systems. These transferable Boltzmann Generators allow approximate sampling from the target distribution of unseen systems, as well as efficient reweighting to the target Boltzmann distribution. The transferability of the proposed framework is evaluated on dipeptides, where we show that it generalizes efficiently to unseen systems. Furthermore, we demonstrate that our proposed architecture enhances the efficiency of Boltzmann Generators trained on single molecular systems.

翻译：分子系统的平衡样本生成一直是统计物理学中长期存在的问题。玻尔兹曼生成器是一种生成式机器学习方法，它通过学习一个从简单先验分布到目标玻尔兹曼分布的正则化流变换来解决这一问题。最近，流匹配方法已被用于在笛卡尔坐标系下为小分子系统训练玻尔兹曼生成器。我们扩展了这项工作，提出了首个可在化学空间内迁移的玻尔兹曼生成器框架，使其能够无需针对测试分子系统进行重新训练，即可零样本预测其玻尔兹曼分布。这些可迁移的玻尔兹曼生成器允许从未见系统的目标分布中进行近似采样，并能高效地重新加权至目标玻尔兹曼分布。我们在二肽分子上评估了所提出框架的迁移能力，结果表明该框架能高效泛化至未见系统。此外，我们证明了所提出的架构提升了针对单一分子系统训练的玻尔兹曼生成器的效率。