论文标题
了解预测时间延迟和偏见传播器在狂欢中的作用
Understanding the role of predictive time delay and biased propagator in RAVE
论文作者
论文摘要
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))并分析并正式化其一些近似值。其中包括：（a）选择预测时间延迟，或者ML应该尝试预测MD的给定系统输出的状态，以及（b）在短时间内，在近似偏见的传播器中，将动态的传播器作为无偏向的传播器而产生多少误差。我们通过主方程框架来证明，只要采用一个小的非零值，为什么确切的时间段选择是无关紧要的。我们还得出了重新享受偏见的繁殖者的校正，并在某种程度上对我们的不满，但同时也为了放心，发现它几乎没有影响我们先前推出和使用的直觉图片。
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.
