机器学习动力学表示加速 RiteWeight 收敛
Machine-Learned Dynamical Representations for Accelerated RiteWeight Convergence
AI总结：
本研究比较了 DeepTICA 与 SPIB-VAE 两种机器学习表示在 RiteWeight 重加权中的表现，发现 DeepTICA 在有限超参数下更稳健，并提出了基于粗粒度 MSM 的动力学评分用于高效选择表示与超参数。
AI中文摘要：
生成模型的日益普及使得短分子动力学轨迹集合越来越常见，这产生了对能够从不恰当加权的构象系综中恢复具有物理意义的稳态布居和动力学的日益增长的需求。随机迭代轨迹重加权（RiteWeight）通过重复随机聚类和迭代重加权来解决这一问题，无需传统马尔可夫状态模型（MSM）中使用的固定马尔可夫离散化。然而，RiteWeight 执行随机聚类所采用的降维特征空间的选择尚未被系统研究。在此，我们比较了两种机器学习表示，DeepTICA 和 SPIB-VAE，与线性 TICA 在从有缺陷的分布中恢复稳态可观测量方面的表现。DeepTICA 通过靶向慢转移算子本征模来学习非线性坐标，而 SPIB-VAE 将构象压缩到低维潜空间，同时保留可预测未来亚稳态的信息。在有限的超参数探索下，DeepTICA 提供了相对稳健的 RiteWeight 表示，而 SPIB-VAE 则从更广泛的优化中获益更多。此外，由与 RiteWeight 随机聚类分辨率相当的粗粒度 MSM 计算出的动力学评分，为高效选择 RiteWeight 的降维表示及其超参数提供了有用标准。
英文摘要：
The increasing use of generative models has made ensembles of short molecular dynamics trajectories increasingly common, creating a growing need for methods that can recover physically meaningful steady-state populations and kinetics from improperly weighted conformational ensembles. Randomized Iterative Trajectory Reweighting (RiteWeight) addresses this problem through repeated random clustering and iterative reweighting, without requiring the fixed Markovian discretization used in conventional Markov state models (MSM). However, the choice of reduced feature space in which RiteWeight performs random clustering has not been systematically investigated. Here, we compare two machine-learned representations, DeepTICA and SPIB-VAE, with linear TICA for recovering steady-state observables from flawed distributions. DeepTICA learns nonlinear coordinates by targeting slow transfer-operator eigenmodes, whereas SPIB-VAE compresses configurations into a low-dimensional latent space while retaining information predictive of future metastable states. DeepTICA provided a comparatively robust RiteWeight representation under limited hyperparameter exploration, whereas SPIB-VAE benefited more strongly from broader optimization. Moreover, a kinetic score computed from a coarse MSM at a resolution comparable to that used for RiteWeight random clustering provided a useful criterion for efficiently selecting reduced representations and their hyperparameters for RiteWeight.
