电解质
聚合物
材料科学
放松(心理学)
离子电导率
粘度
化学工程
计算机科学
化学
复合材料
物理化学
心理学
电极
社会心理学
工程类
作者
Bill K. Wheatle,Erick F. Fuentes,Nathaniel A. Lynd,Venkat Ganesan
出处
期刊:Macromolecules
[American Chemical Society]
日期:2020-10-21
卷期号:53 (21): 9449-9459
被引量:29
标识
DOI:10.1021/acs.macromol.0c01547
摘要
We apply a machine learning (ML) technique to the multiobjective design of polymer blend electrolytes. In particular, we are interested in maximizing electrolyte performance measured by a combination of ionic transport (measured by ionic conductivity) and electrolyte mechanical properties (measured by viscosity) in a coarse-grained molecular dynamics framework. Recognizing the expense of evaluating each of these properties, we identify that the anionic mean-squared displacement and polymer relaxation time can serve as their proxies. By employing the ML approach known as Bayesian optimization, we identify a trade-off between ion transport and electrolyte mechanical properties as a function of varied design parameters, which include host molecular weight and polarity. Our results suggest that blend electrolytes whose hosts have unequal molecular weights, such as gel polymer electrolytes, rarely maximize electrolyte performance. Overall, our results suggest the potential of a framework to design high-performance electrolytes using a combination of molecular simulation and ML.
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