Tian Lumin, Wang Wentan, Ji Xiaobo, Xu Zhibin, Zhou Wenyan, Lu Wencong
College of Sciences, Shanghai University, Shanghai 200444, China.
College of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Materials (Basel). 2025 May 23;18(11):2437. doi: 10.3390/ma18112437.
The Curie temperature (Tc) of LaMnO-based perovskites is one of the most important properties associated with their magnetic and spintronic applications. The search for new perovskites with even higher Tc is a challenging problem in material design. Through the systematic optimization of support vector regression (SVR) architecture, we establish a predictive framework for determining the Curie temperature (Tc) of doped LaMnO perovskites, leveraging fundamental atomic descriptors. The correlation coefficient (R) between the predicted and experimental Curie temperatures demonstrated high values of 0.9111 when evaluated through the leave-one-out cross-validation (LOOCV) approach, while maintaining a robust correlation of 0.8385 on the independent test set. The subsequent high-throughput screening of perovskite compounds exhibiting higher Curie temperatures was implemented via our online computation platform for materials data mining (OCPMDM), enabling the rapid identification of candidate materials through systematic screening protocols. The findings demonstrate that machine learning exhibits significant efficacy and cost-effectiveness in identifying lanthanum manganite perovskites with elevated Tc, as validated through comparative computational and empirical analyses. Furthermore, a web-based computational infrastructure is implemented for the global dissemination of the predictive framework, enabling the open-access deployment of the validated machine learning model.
基于镧锰氧化物的钙钛矿的居里温度（Tc）是与其磁学和自旋电子学应用相关的最重要特性之一。寻找具有更高居里温度的新型钙钛矿是材料设计中的一个具有挑战性的问题。通过对支持向量回归（SVR）架构进行系统优化，我们利用基本原子描述符建立了一个用于确定掺杂镧锰钙钛矿居里温度（Tc）的预测框架。通过留一法交叉验证（LOOCV）方法评估时，预测居里温度与实验居里温度之间的相关系数（R）显示出高达0.9111的值，而在独立测试集上保持了0.8385的稳健相关性。随后，通过我们的材料数据挖掘在线计算平台（OCPMDM）对具有更高居里温度的钙钛矿化合物进行了高通量筛选，通过系统筛选协议能够快速识别候选材料。研究结果表明，通过比较计算和实证分析验证，机器学习在识别具有较高居里温度的镧锰钙钛矿方面具有显著的功效和成本效益。此外，还实施了基于网络的计算基础设施，以全球传播预测框架，实现经过验证的机器学习模型的开放获取部署。
