1. Materials Genome Institute, Shanghai University, Shanghai 200444, China
2. Department of Chemistry, College of Sciences, Shanghai University,Shanghai 200444, China
ABO3 perovskite manganates has become the most popular memory material in anti-ferromagnets due to its low cost and good stability. It is of great significance to improve the Néel temperature (TN) of ABO3 perovskite manganates to make it antiferromagnetic at room temperature. In this work, hyper-polyhedron method is used to rank the importance of characteristic variables, and the machine learning algorithm is integrated to screen features.The online prediction platform was built for TN of ABO3 perovskite manganates. The XGBoost machine learning model was established to screen out the potential material（Sr0.7Ce0.1Sm0.2MnO3, 308.5 K） with the predicted TN higher than room temperature based on high-throughput screening. The TN of the potential material is 6.37% higher than the highest one known. This research method is helpful for experimental workers to select the most promising materials, which can be used to speed up the research and development of new materials with targeted performances.