Performing computational molecular dynamics (MD) simulations of small-molecule systems has become one of the most prominently used methods in studies of molecular structure and behavior. However, MD is a computationally expensive and time-consuming methodology because of the requirement of computing detailed interactions among atom-atom pairs. There is great interest, therefore, in reducing the time and computational power needed to approximate real-world systems. Most commonly, such efforts have employed machine learning techniques to predict extensive properties of molecular systems. Here, we propose accelerating simulations by predicting conformational changes - a prospect that has not yet been fully explored. Previous work attempted applying a linear dynamical analysis algorithm named Dynamic Mode Decomposition to MD data, which has been shown to be ineffective through a multiresolution analysis. We propose herein the use of Sparse Identification of Nonlinear Dynamical Systems (SINDy), a nonlinear model which has been shown to accurately decipher the governing equations of dynamical systems. We will be testing the effectiveness of SINDy with MD data by performing an iterative error analysis while varying the initial parameters of the dataset, thereby gaining a better understanding of how much data (and in what form) should be inputted to maximize the accuracy of a simulated SINDy model of an MD dataset. If shown to be sufficiently accurate, we then can implement SINDy simultaneously with MD in an active learning loop to save time and computational power while maintaining a high degree of predictive capability for peptide conformations. The current goal is to obtain a deeper understanding of peptide conformational changes that could, in the future, be combined with machine learning techniques to greatly accelerate classical MD simulations.
This project is supported by the UW Computational Neuroscience Center, and the DMREF Program of NSF through the MGI platform under DMR# 1629071, 1848911, and 1922020.