Machine Learning Model for High-Throughput Screening of Perovskite Manganites with the Highest Neel Temperature

Neel temperature (T-N), the antiferromagnetic to paramagnetic phase transition temperature, is also one of the important magnetic properties of antiferromagnets. Antiferromagnets could be used to write and read information below T-N in electronic memory devices. Perovskite manganites are an important antiferromagnetic material with superior magnetic properties. However, it is challengeable to seek novel perovskite manganites with T-N higher than room temperature by trial-and-error method. In this work, the maximum correlation minimum redundancy (mRMR) integrated machine learning approaches were used to screen the optimal subset of features, which included chemical compositions and atomic parameters of perovskite manganites. The machine learning model called support vector regression (SVR) was constructed to predict the T-N of perovskite manganites. The correlation coefficient (R) between experimental T-N and predicted T-N reached as high as 0.87 for the training set in leave-one-out cross-validation (LOOCV) and 0.86 for the independent testing set, respectively. The high-throughput screening of new perovskites with higher T-N temperature was then carried out by using our online computation platform for materials data mining (OCPMDM). The T-N of designed perovskite manganites (Sr0.7Pm0.3MnO3) was predicted to be 307.5K, increasing by 6% compared to the maximum T-N of Sr0.9Ce0.1MnO3 (290K) reported.