Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

TL;DR

This paper introduces a multi-task graph neural network that accelerates the screening of amorphous polymer electrolytes by learning from noisy short MD simulations and a small set of long, converged MD data, enabling large-scale material discovery.

Contribution

The study presents a novel multi-task GNN approach that effectively predicts properties from noisy MD data, significantly reducing computational costs in amorphous polymer screening.

Findings

Accurately predicts 4 key properties of polymers

Screens over 6200 polymers efficiently

Provides insights into polymer design principles

Abstract

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.