Xu Pengcheng, Chang Dongping, Lu Tian, Li Long, Li Minjie, Lu Wencong
Materials Genome Institute, Shanghai University, and Shanghai Materials Genome Institute, Shanghai 200444, China.
Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.
J Chem Inf Model. 2022 Nov 14;62(21):5038-5049. doi: 10.1021/acs.jcim.1c00566. Epub 2021 Aug 10.
Ferroelectric perovskites are one of the most promising functional materials due to the pyroelectric and piezoelectric effect. In the practical applications of ferroelectric perovskites, it is often necessary to meet the requirements of multiple properties. In this work, a multiproperties machine learning strategy was proposed to accelerate the discovery and design of new ferroelectric ABO-type perovskites. First, a classification model was constructed with data collected from publications to distinguish ferroelectric and nonferroelectric perovskites. The classification accuracies of LOOCV and the test set are 87.29% and 86.21%, respectively. Then, two machine learning strategies, Machine-Learning Workflow and SISSO, were used to construct the regression models to predict the specific surface area (SSA), band gap (), Curie temperature (), and dielectric loss (tan δ) of ABO-type perovskites. The correlation coefficients of LOOCV in the optimal models for SSA, , and are 0.935, 0.891, and 0.971, respectively, while the correlation coefficient of the predicted and experimental values of the SISSO model for tan δ prediction could reach 0.913. On the basis of the models, 20 ABO ferroelectric perovskites with three different application prospects were screened out with the required properties, which could be explained by the patterns between the important descriptors and the properties by using SHAP. Furthermore, the constructed models were developed into web servers for the researchers to accelerate the rational design and discovery of ABO ferroelectric perovskites with desired multiple properties.
铁电钙钛矿由于其热释电和压电效应，是最具潜力的功能材料之一。在铁电钙钛矿的实际应用中，通常需要满足多种性能要求。在这项工作中，提出了一种多性能机器学习策略，以加速新型铁电ABO型钙钛矿的发现和设计。首先，利用从文献中收集的数据构建了一个分类模型，以区分铁电和非铁电钙钛矿。留一法交叉验证（LOOCV）和测试集的分类准确率分别为87.29%和86.21%。然后，使用两种机器学习策略，即机器学习工作流程（Machine-Learning Workflow）和单信息素筛选算法（SISSO），构建回归模型，以预测ABO型钙钛矿的比表面积（SSA）、带隙（）、居里温度（）和介电损耗（tan δ）。在SSA、和的最优模型中，留一法交叉验证的相关系数分别为0.935、0.891和0.971，而SISSO模型预测tan δ的预测值与实验值的相关系数可达0.913。基于这些模型，筛选出了20种具有三种不同应用前景且具备所需性能的ABO铁电钙钛矿，利用SHAP可通过重要描述符与性能之间的模式对其进行解释。此外，将构建的模型开发成网络服务器，供研究人员加速合理设计和发现具有所需多种性能的ABO铁电钙钛矿。
