Neural Network Based Molecular Dynamics to Study Polymers

Polymers are an important class of materials that display morphological complexity and diverse inter-atomic interactions. These two factors have defied large-scale and long-time quantum-accurate atomic-level simulations of polymer dynamics. Traditional simulation methods utilize parameterized classical potentials or force fields which often lack accuracy, transferability, and versatility. Moreover, although these methods are known to fail in notable circumstances, it is not clear how the traditional methods can be systematically improved using the known failures. Neural network based models for molecular dynamics, the subject of this study, are capable of learning from reference quantum mechanical data. Once learned, these models can emulate the parent quantum calculations in accuracy, but be about a billion orders of magnitude faster. Neural network based molecular dynamics simulations can thus reach length-scales and time-scales previously inaccessible using quantum mechanical methods. In this work, we develop a new class of first-ever neural network models for the prototypical case of hydrocarbons and provide several meticulous and diverse validation tests. Challenges that remain are discussed and pathways to overcome such challenges are presented.