  Machine learning has been widely adopted to accelerate the screening of
materials. Most existing studies implicitly assume that the training data are
generated through a deterministic, unbiased process, but this assumption might
not hold for the simulation of some complex materials. In this work, we aim to
screen amorphous polymer electrolytes which are promising candidates for the
next generation lithium-ion battery technology but extremely expensive to
simulate due to their structural complexity. We demonstrate that a multi-task
graph neural network can learn from a large amount of noisy, biased data and a
small number of unbiased data and reduce both random and systematic errors in
predicting the transport properties of polymer electrolytes. This observation
allows us to achieve accurate predictions on the properties of complex
materials by learning to reduce errors in the training data, instead of running
repetitive, expensive simulations which is conventionally used to reduce
simulation errors. With this approach, we screen a space of 6247 polymer
electrolytes, orders of magnitude larger than previous computational studies.
We also find a good extrapolation performance to the top polymers from a larger
space of 53362 polymers and 31 experimentally-realized polymers. The strategy
employed in this work may be applicable to a broad class of material discovery
problems that involve the simulation of complex, amorphous materials.
