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

Tian Xie (), Arthur France-Lanord, Yanming Wang, Jeffrey Lopez, Michael A. Stolberg, Megan Hill, Graham Michael Leverick, Rafael Gomez-Bombarelli, Jeremiah A. Johnson, Yang Shao-Horn and Jeffrey C. Grossman ()
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Tian Xie: Massachusetts Institute of Technology
Arthur France-Lanord: Massachusetts Institute of Technology
Yanming Wang: Massachusetts Institute of Technology
Jeffrey Lopez: Massachusetts Institute of Technology
Michael A. Stolberg: Massachusetts Institute of Technology
Megan Hill: Massachusetts Institute of Technology
Graham Michael Leverick: Massachusetts Institute of Technology
Rafael Gomez-Bombarelli: Massachusetts Institute of Technology
Jeremiah A. Johnson: Massachusetts Institute of Technology
Yang Shao-Horn: Massachusetts Institute of Technology
Jeffrey C. Grossman: Massachusetts Institute of Technology

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-30994-1

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