论文标题
机器学习辅助发现的锂离子电池超级英里型固态电解质
Machine Learning-Aided Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries
论文作者
论文摘要
锂离子固态电解质（LI-SSE）是一种有前途的解决方案，可以解决常规锂离子电池（LIB）的关键问题，例如较差的离子电导率，界面不稳定性和树突生长。在这项研究中，开发了一个由高通量筛查和机器学习的替代模型组成的平台，用于在20,237个含Li的材料中发现超离子Li-SS。对于训练数据库，从先前的文献中获得了NA超离子导体（NASICON）和LI SUPERIONIC指挥（Lisicon）类型SSE的离子电导率。然后，化学描述符（CD）和其他结构特性用作机器可读特征。通过筛选标准选择LI-SSE候选者，并遵循对离子电导率的预测。然后，为了减少替代模型中的不确定性，通过考虑最出色的两个模型来使用合奏方法，其平均预测准确性分别为0.843和0.829。此外，进行了第一原理计算，以确认强候选者的离子电导率。最后，提出了以前未研究的六个潜在的超级离子Li-SSE。我们认为，构造的平台可以以最低的成本加速具有高离子电导率的LI-SS。
Li-Ion Solid-State Electrolytes (Li-SSEs) are a promising solution that resolves the critical issues of conventional Li-Ion Batteries (LIBs) such as poor ionic conductivity, interfacial instability, and dendrites growth. In this study, a platform consisting of a high-throughput screening and a machine-learning surrogate model for discovering superionic Li-SSEs among 20,237 Li-containing materials is developed. For the training database, the ionic conductivity of Na SuperIonic CONductor (NASICON) and Li SuperIonic CONductor (LISICON) type SSEs are obtained from the previous literature. Then, the chemical descriptor (CD) and additional structural properties are used as machine-readable features. Li-SSE candidates are selected through the screening criteria, and the prediction on the ionic conductivity of those is followed. Then, to reduce uncertainty in the surrogate model, the ensemble method by considering the best-performing two models is employed, whose mean prediction accuracy is 0.843 and 0.829, respectively. Furthermore, first-principles calculations are conducted for confirming the ionic conductivity of the strong candidates. Finally, six potential superionic Li-SSEs that have not previously been investigated are proposed. We believe that the constructed platform can accelerate the search for Li-SSEs with high ionic conductivity at minimum cost.
