了解预测时间延迟和有偏传播器在RAVE中的作用
Understanding the role of predictive time delay and biased propagator in RAVE
摘要：
在这项工作中，我们重新审视了我们最近的迭代机器学习（ML）-分子动力学（MD）技术“用于增强采样的重加权自动编码变分贝叶斯（RAVE）”（Ribeiro，Bravo，Wang，Tivary，J. Chem. Phys. 149 072301（2018）和Wang，Ribeiro，Tivary，Nature Commun. 10 3573（2019）），并分析和形式化它的一些近似。这些措施包括：（a）预测时间延迟的选择，或者ML应该在未来多远尝试预测来自MD的给定系统输出的状态，以及（b）对于短时间延迟，在将动态的有偏传播器近似为无偏传播器时产生了多大的误差。我们通过一个主方程框架来证明为什么只要采用一个小的非零值，时间延迟的确切选择就无关紧要。我们还得到了一个修正，以重新加权有偏传播子，有点让我们不满，但也让我们放心，发现它几乎没有区别，我们以前推导和使用的直观图像。
英文摘要：
In this work, we revisit our recent iterative machine learning (ML) -- molecular dynamics (MD) technique "Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)" (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 149 072301 (2018) and Wang, Ribeiro, Tiwary, Nature Commun. 10 3573 (2019)) and analyze as well as formalize some of its approximations. These including: (a) the choice of a predictive time-delay, or how far into the future should the ML try to predict the state of a given system output from MD, and (b) for short time-delays, how much of an error is made in approximating the biased propagator for the dynamics as the unbiased propagator. We demonstrate through a master equation framework as to why the exact choice of time-delay is irrelevant as long as a small non-zero value is adopted. We also derive a correction to reweight the biased propagator, and somewhat to our dissatisfaction but also to our reassurance, find that it barely makes a difference to the intuitive picture we had previously derived and used.
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