LaGaO_3基钙钛矿型固体电解质氧离子导电能力的QSPR研究

QSPR studies on oxide ion conductivity of perovskites electrolyte based on doped LaGaO_3

本文从文献中收集了多个钙钛矿结构的掺杂LaGaO_3系列氧离子导体电解质材料样本,以导电率的对数Ln(?)为目标,使用各种机器学习方法进行回归分析,包括多元线性回归(MLR)、偏最小二乘法(PLS)和支持向量回归(SVR),建立了Ln(?)与其分子结构参数之间的定量模型。结果表明:SVR方法所得导电率Ln(?)的留一法预报结果与实验最相符,计算值与实验值的相关系数为0.911。使用独立测试集预报的计算值和实验值的相关系数为0.880。此外还用建立的模型对La_(1-x)Sr_xGa_(1-y)Mg_yO_3掺杂体系的导电率进行了预报,根据预报结果做出的等高面图显示的优区与实验所得结果一致。

In this paper, Molecular parameters and ionic conductivities data of perovskite-type oxides of doped LaGaO3 series were collected from literatures and experiments. The relationship between the electrical conductivities and the molecular parameters was examined. The data were processed by several machine learning methods, including Multiple Linear Regression （ MLR）, Partial Least Squares （PIS） and Support Vector Regression （SVR）. The conductivities values of SVR leave-one-out cross validation were corresponded with the values of experiments. The correlation coefficient of calculated values and experimental values was 0. 911. Independ- ent test set was used for prediction, and the correlation coefficient of values of prediction and experiment was 0. 880. The model was used in the prediction of doped system of La1-xSrxGa1-yMgyO3. A diagram of contour was obtained from the results of prediction. It showed excellent agreement with experimental results.