It has long remained challenging to predict the properties of complex chemical systems, such as polymer-based materials and their composites. We constructed currently the largest database of lithium conducting solid polymer electrolytes (104 entries) and employed a transfer learned, graph neural network to accurately predict their conductivity (mean absolute error of less than 1 in a logarithmic scale). The bias-free prediction by the network helped us to find out superionic conductors, composed of charge transfer complexes of aromatic polymers (ionic conductivity of around 10-3 S/cm at room temperature). The glassy design was against the traditional rubbery concept of polymer electrolytes, but found to be appropriate to achieve the fast, decoupled motion of ionic species from polymer chains, and to enhance thermal and mechanical stability. The unbiased suggestions by machine learning models are helpful for researches to discover unexpected chemical phenomena, which would also induce the paradigm shift of energy-related functional materials.

中文翻译：

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AI辅助探索具有芳香结构的超离子玻璃型Li+导体

长期以来，预测复杂化学系统（例如聚合物基材料及其复合材料）的特性一直具有挑战性。我们构建了目前最大的锂导电固体聚合物电解质数据库（104 个条目），并采用迁移学习的图神经网络来准确预测它们的电导率（对数标度的平均绝对误差小于 1）。网络的无偏置预测帮助我们找到了由芳香聚合物的电荷转移复合物组成的超离子导体（室温下离子电导率约为 10-3 S/cm）。玻璃状设计与聚合物电解质的传统橡胶概念背道而驰，但发现适合实现聚合物链中离子物质的快速、解耦运动，并提高热稳定性和机械稳定性。