无定形固体
计算机科学
分子动力学
电解质
放松(心理学)
聚合物
噪音(视频)
材料科学
人工智能
化学
计算化学
心理学
社会心理学
图像(数学)
有机化学
电极
物理化学
复合材料
作者
Tian Xie,Arthur France‐Lanord,Yanming Wang,Jeffrey Lopez,Michael A. Stolberg,Megan R. Hill,Graham Leverick,Rafael Gómez‐Bombarelli,Jeremiah A. Johnson,Yang Shao‐Horn,Jeffrey C. Grossman
标识
DOI:10.1038/s41467-022-30994-1
摘要
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.
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