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<p>Perovskite magnetic materials have garnered significant attention due to their excellent applications in spintronics and magnetic storage. However, their discovery via conventional methods often entails considerable computational cost and time. This study employs machine learning (ML) to develop a classification model for magnetic ground states and a regression model for predicting the magnetic moments of perovskite materials, utilizing a dataset of 1511 ABX<sub>3</sub> compounds. We systematically evaluated four decision tree algorithms: Extra Trees (ET), Gradient Boosting (GB), eXtreme Gradient Boosting (XGB), and Random Forest (RF). The XGB classification (XGBC) model exhibited the best performance (accuracy = 0.946) for classifying ferromagnetic (FM), ferrimagnetic (FiM), antiferromagnetic (AFM), and non-magnetic (NM) states. While the XGB regression (XGBR) model demonstrated excellent performance (maximum R² = 0.919) in predicting magnetic moments. The XGBC model was subsequently applied to classify the magnetism of 304 new ABX<sub>3</sub> compounds, further validating its effectiveness as a screening tool for identifying perovskite materials with distinct magnetic properties. Finally, SHapley Additive exPlanations (SHAP) analysis underscored the significant role of perovskite’s magnetization in magnetism. This study highlights ML’s potential for analyzing ABX<sub>3</sub> perovskites magnetism and provides a valuable approach to accelerating the discovery and design of novel ABX<sub>3</sub> perovskite magnetic materials.</p>

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Classification of magnetic ground States and prediction of magnetic moments for ABX3 perovskites utilizing machine learning techniques

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Yunxia Hao,
  • Jishun Zhang,
  • Xi Sun,
  • Zihan Qu,
  • Zuyu Xu,
  • Yunlai Zhu,
  • Yuehua Dai

摘要

Perovskite magnetic materials have garnered significant attention due to their excellent applications in spintronics and magnetic storage. However, their discovery via conventional methods often entails considerable computational cost and time. This study employs machine learning (ML) to develop a classification model for magnetic ground states and a regression model for predicting the magnetic moments of perovskite materials, utilizing a dataset of 1511 ABX3 compounds. We systematically evaluated four decision tree algorithms: Extra Trees (ET), Gradient Boosting (GB), eXtreme Gradient Boosting (XGB), and Random Forest (RF). The XGB classification (XGBC) model exhibited the best performance (accuracy = 0.946) for classifying ferromagnetic (FM), ferrimagnetic (FiM), antiferromagnetic (AFM), and non-magnetic (NM) states. While the XGB regression (XGBR) model demonstrated excellent performance (maximum R² = 0.919) in predicting magnetic moments. The XGBC model was subsequently applied to classify the magnetism of 304 new ABX3 compounds, further validating its effectiveness as a screening tool for identifying perovskite materials with distinct magnetic properties. Finally, SHapley Additive exPlanations (SHAP) analysis underscored the significant role of perovskite’s magnetization in magnetism. This study highlights ML’s potential for analyzing ABX3 perovskites magnetism and provides a valuable approach to accelerating the discovery and design of novel ABX3 perovskite magnetic materials.