发表机构
University of Maryland; Institute for Physical Science and Technology; University of Maryland Institute for Health Computing（马里兰大学; 物理科学与技术研究所; 马里兰大学健康计算研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SPIBER结合SPIB与GFlowNets，从短未收敛轨迹中学习集体变量并估计自由能差异，在多个体系上误差在一个热能单位内。
AI 中文摘要
分子系统具有许多自由度，但其亚稳态行为通常可以用少数几个集体变量来描述。从有限的模拟数据中识别这些变量并估算沿这些变量的自由能，仍然是一个具有挑战性的重要问题。独立的短轨迹可能采样到不同的亚稳态，而不会捕捉到转变或建立它们之间的相对平衡布居。对于在单一温度下使用相同哈密顿量生成的无偏轨迹，基于直方图重加权的替代方法无法纠正这种不平衡。在此，我们提出SPIBER，它结合了状态预测信息瓶颈（SPIB）与生成流网络（GFlowNets）。SPIB利用深度学习通过过去-未来信息瓶颈近似慢自由度，保留预测未来亚稳态所需的信息。我们表明，这种压缩限制了布居区域中条件熵的变化，使得更容易计算的条件平均势能可用于近似自由能差异。在获得足够的局部采样以估算这些能量的情况下，它们定义了GFlowNets的目标分布，GFlowNets是基于能量的生成采样器，根据估计的热力学稳定性而非观测布居进行采样。对于径向双阱势中的粒子、丙氨酸二肽以及九残基肽AIB9，SPIBER恢复采样亚稳态之间的自由能差异，误差在参考值的一个热能单位以内。该方法在多达四个潜在维度中结合了集体变量学习和自由能估计，无需收敛的状态布居或额外的分子动力学模拟。
英文摘要
Molecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables. Identifying these variables and estimating free energies along them from limited simulation data remains a challenging, important problem. Separate short trajectories may sample different metastable states without capturing transitions or establishing their relative equilibrium populations. For unbiased trajectories generated with the same Hamiltonian at a single temperature, alternate methods based on histogram reweighting cannot correct this imbalance. Here we present SPIBER, which combines the State Predictive Information Bottleneck (SPIB) with Generative Flow Networks (GFlowNets). SPIB uses deep learning to approximate slow degrees of freedom through a past-future information bottleneck, retaining information needed to predict future metastable states. We show that this compression limits conditional entropy variations in populated regions, allowing conditional mean potential energies, which are much easier to calculate, to be used to approximate free energy differences. Given sufficient local sampling to estimate these energies, they define the target distribution for GFlowNets, energy-based generative samplers that sample according to estimated thermodynamic stability rather than observed populations. For a particle in a radial double-well potential, for alanine dipeptide, and for the nine-residue peptide AIB9, SPIBER recovers free energy differences between sampled metastable states to within one thermal energy unit of reference values. The method combines collective-variable learning and free energy estimation in up to four latent dimensions, without requiring converged state populations or additional molecular dynamics simulations.
CommentsJournal-Style Article 25 pages (13 in main manuscript, 12 in supporting information) with 14 figures (6 in main manuscript, 8 in supporting information)
