We present a machine-learning model based on normalizing flows that is trained to sample from the isobaric-isothermal ensemble. In our approach, we approximate the joint distribution of a fully-flexible triclinic simulation box and particle coordinates to achieve a desired internal pressure. This novel extension of flow-based sampling to the isobaric-isothermal ensemble yields direct estimates of Gibbs free energies. We test our NPT-flow on monatomic water in the cubic and hexagonal ice phases and find excellent agreement of Gibbs free energies and other observables compared with established baselines.

翻译：我们提出了一种基于归一化流的机器学习模型，该模型被训练用于从等压等温系综中进行采样。在我们的方法中，我们近似了全柔性三斜模拟盒与粒子坐标的联合分布，以达到所需的内部压力。这种将基于流的采样扩展到等压等温系综的新方法能够直接估计吉布斯自由能。我们在立方晶系和六方晶系冰相的单原子水上测试了我们的NPT流模型，发现吉布斯自由能及其他可观测量与已有基准方法相比具有极好的一致性。