Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
复制标题
DOI：
10.1126/science.aaw1147
复制
发表时间：
2019-09-06
期刊：
影响因子：
56.9
通讯作者：
Wu, Hao
中科院分区：
文献类型：
--
作者：
Noe, Frank;Olsson, Simon;Wu, Hao
计算凝聚态多体系统的平衡态，如溶剂化蛋白质，是一个长期存在的挑战。在“一次操作”中缺乏生成统计上独立的平衡样本的方法：投入大量的计算工作来以小步骤模拟这些系统，例如使用分子动力学。结合深度学习和统计力学，我们开发了Boltzmann生成器，它被证明可以生成典型凝聚态系统和蛋白质的无偏一次平衡样本。Boltzmann生成器使用神经网络来学习复杂的构型平衡分布到易于采样的分布的坐标变换。演示了自由能差的精确计算和新构型的发现，提供了一种统计力学工具，可以在采样期间避免在没有反应坐标先验知识的情况下发生罕见事件。
Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in "one shot:" vast computational effort is invested for simulating these systems in small steps, e.g., using molecular dynamics. Combining deep learning and statistical mechanics, we developed Boltzmann generators, which are shown to generate unbiased one-shot equilibrium samples of representative condensed-matter systems and proteins. Boltzmann generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free-energy differences and discovery of new configurations are demonstrated, providing a statistical mechanics tool that can avoid rare events during sampling without prior knowledge of reaction coordinates.