Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning.

ABSTRACT: Machine learning techniques including neural networks are popular tools for chemical, physical and materials applications searching for viable alternative methods in the analysis of structure and energetics of systems ranging from crystals to biomolecules. Efforts are less abundant for prediction of kinetics and dynamics. Here we explore the ability of three well established recurrent neural network architectures for reproducing and forecasting the energetics of a liquid solution of ethyl acetate containing a macromolecular polymer-lipid aggregate at ambient conditions. Data models from three recurrent neural networks, ERNN, LSTM and GRU, are trained and tested on half million points time series of the macromolecular aggregate potential energy and its interaction energy with the solvent ob

SUBMITTER: Andrews J 

PROVIDER: S-EPMC9200117 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature