A generalized deep learning approach for local structure identification in molecular simulations.

ABSTRACT: Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (e.g., ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (e.g., crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires

SUBMITTER: DeFever RS 

PROVIDER: S-EPMC6839808 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature