Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties
This accelerates screening for polymer electrolytes in lithium-ion batteries, though it is incremental as it applies existing ML methods to a specific bottleneck in materials science.
The researchers tackled the problem of expensive molecular dynamics simulations for amorphous polymer electrolytes by developing a multi-task graph neural network that learns from noisy short simulations and a few converged long ones, achieving accurate predictions of 4 properties and screening 6247 polymers—orders of magnitude more than previous studies.
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.