Neural network predictions of energy transfer in macromolecules

A neural network is trained to predict the dynamical behavior of internal energy in macromolecules. Energy flow out of localized sites within a polyethylene chain is studied as a function of time, excitation level, and temperature. Computations were examined for CH stretch excitation levels between υ=2 and 14, temperatures between T=20 and 350 K, and time from 0 to 200 ps. The results show that the neural network predictions are accurate to within 8% error of those calculated from molecular dynamics. This difference is related to statistical fluctuations in the molecular dynamics calculations which result from small ensemble averages