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Polymer electrolytes are promising candidates for the next generationlithium-ion battery technology. Large scale screening of polymer electrolytesis hindered by the significant cost of molecular dynamics (MD) simulation inamorphous systems: the amorphous structure of polymers requires multiple,repeated sampling to reduce noise and the slow relaxation requires longsimulation time for convergence. Here, we accelerate the screening with amulti-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. Weachieve accurate predictions of 4 different converged properties and screen aspace of 6247 polymers that is orders of magnitude larger than previouscomputational studies. Further, we extract several design principles forpolymer electrolytes and provide an open dataset for the community. Ourapproach could be applicable to a broad class of material discovery problemsthat involve the simulation of complex, amorphous materials.

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