Searching for Mechanically Superior Solid-State Electrolytes in Li-Ion Batteries via Data-Driven Approaches
Searching for Mechanically Superior Solid-State Electrolytes in Li-Ion Batteries via Data-Driven Approaches
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DOI：
10.1021/acsami.1c07999
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发表时间：
2021-09-02
影响因子：
9.5
通讯作者：
Min, Kyoungmin
中科院分区：
文献类型：
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作者：
Choi, Eunseong;Jo, Junho;Min, Kyoungmin
锂离子固态电解质（ssi）具有巨大的潜力，但由于界面接触稳定性问题和枝晶的形成和生长，其商业化受到限制。在这项研究中，采用机器学习回归算法从17619名候选人中筛选机械上优越的sse。从可用的数据库中导入弹性信息（14,238个结构），并使用物理化学和结构特性构建它们的机器学习描述符。预测剪切模量和体模量的代理模型的R-2值分别为0.819和0.863。将构建的模型应用于预测潜在sse的弹性特性，并进行第一性原理计算验证。此外，主动学习过程的应用减少了预测的不确定性，根据模量的类型，仅添加32-63%的新数据集，就可以将R-2分数从大约0.6-0.8提高到0.8。我们相信，目前的模型和额外的数据集可以加速寻找最优sse的过程，以满足所寻求的力学条件。
Li-ion solid-state electrolytes (SSEs) have great potential, but their commercialization is limited due to interfacial contact stability issues and the formation and growth of dendrites. In this study, a machine learning regression algorithm was implemented to screen for mechanically superior SSEs among 17,619 candidates. Elasticity information (14,238 structures) was imported from an available database, and their machine learning descriptors were constructed using physiochemical and structural properties. A surrogate model for predicting the shear and bulk moduli exhibited R-2 values of 0.819 and 0.863, respectively. The constructed model was applied to predict the elastic properties of potential SSEs, and first-principles calculations were conducted for validation. Furthermore, the application of an active learning process, which reduced the prediction uncertainty, was clearly demonstrated to improve the R-2 score from approximately 0.6-0.8 by adding only 32-63% of new data sets depending on the type of modulus. We believe that the current model and additional data sets can accelerate the process of finding optimal SSEs to satisfy the mechanical conditions being sought.