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Magnetic skyrmions, characterized by their topological properties, serve as core components for developing next-generation non-volatile memory devices that demand high density, high speed, and low power consumption. Their formation arises from the Dzyaloshinskii-Moriya interaction (DMI), enabled by non-centrosymmetric structures. Two-dimensional Janus magnetic materials, which inherently break spatial inversion symmetry, readily generate strong DMI, providing an ideal platform for skyrmion generation and novel racetrack memory applications. Within this field, identifying systems with a high Curie temperature ($T_{\rm{C}}$) is crucial, as it directly governs magnetic property stability and application potential under high-temperature conditions. This study integrated literature and open-source databases to construct a dataset of 16, 880 ABC-type two-dimensional materials. Utilizing stoichiometric ratios, intrinsic elemental properties, and electronic structure features as descriptors, four machine learning models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Extra Trees (ET)—were employed for $T_{\rm{C}}$ prediction. Model performance was evaluated via ten-fold cross-validation, revealing that the XGBoost model exhibited superior prediction accuracy and generalization capability. Leveraging this model, $T_{\rm{C}}$ was predicted for 4, 024 unexplored two-dimensional Janus materials. This screening identified 54 promising candidates possessing thermal stability, high magnetic moment, and a $T_{\rm{C}}$ exceeding 300 K. To verify reliability, four candidate systems (EuFeO, GdKTi, DyFeTb, ErFeGd) were randomly selected for theoretical validation using first-principles calculations combined with the Heisenberg model. For systems exhibiting strong correlation effects (containing d-orbital electrons), the Hubbard U parameter was incorporated to describe on-site Coulomb repulsion. Exchange coupling constants were derived using the VASP software package. Subsequently, $T_{\rm{C}}$ values were calculated via classical Monte Carlo simulations performed using the MCSOLVER program. Results demonstrate that the mean absolute error (MAE) of the predicted $T_{\rm{C}}$ agrees well with the model calculations for EuFeO and GdKTi, while larger deviations were observed for DyFeTb and ErFeGd. Nevertheless, the calculated $T_{\rm{C}}$ values for all four candidates surpass room temperature. This work establishes a new computational framework for the efficient screening of high-performance two-dimensional Janus magnetic materials, contributing to the advancement of magnetic storage technologies.
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Keywords:
- machine learning /
- two-dimensional magnetic Janus materials /
- Curie temperature /
- first-principles calculations
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图 1 三步筛选室温本征磁性Janus步骤
Figure 1. Three-step screening room-temperature intrinsic magnetic janus procedure.
图 2 居里温度分布及元素丰度热力图
Figure 2. Heat map of curie temperature distribution and elemental abundance.
图 3 XGB排名前10的特征
Figure 3. Top 10 features of XGB ranking.
图 4 XGB模型(a, b), ET模型(c, d), GBDT模型(e, f), RF模型(g, h)的散点图和误差残差分析图
Figure 4. Scatter plots and error residual analysis plots of XGB model (a, b), ET model (c, d), GBDT model (e, f), and RF model (g, h).
图 5 随机选取的4种janus材料的磁矩随温度变化曲线
Figure 5. Magnetic moment versus temperature curves for four randomly selected janus materials.
图 6 居里温度计算值与预测值柱状图
Figure 6. Bar chart of calculated and predicted Curie temperatures.
表 1 四种算法模型的最优超参数
Table 1. Optimal hyperparameters of the four algorithmic models.
模型 超参数 RF $ D_{{\rm{max}}} $ = 16, $ F_{{\rm{max}}} $ = 0.20, $ N_{{\rm{e}}} $ = 150,
$ L_{{\rm{min}}} $ = 2, $ S_{{\rm{min}}} $ = 6GBDT $ L_{{\rm{r}}} $ = 0.04006057, $ N_{{\rm{e}}} $ = 400, $ D_{{\rm{max}}} $ = 9,
$ Sub $ = 0.8, $ L_{{\rm{min}}} $ = 6, $ S_{{\rm{min}}} $ = 4XGB $ L_{{\rm{r}}} $ = 0.01, $ D_{{\rm{max}}} $ = 14, $ N_{{\rm{e}}} $ = 550,
$ Sub $ = 0.72422896, $\gamma$ = 0.4, $ C_{{\rm{b}}} $ = 0.15ET $ D_{{\rm{max}}} $ = 15, $ F_{{\rm{max}}} $ = 0.46545387, $ N_{{\rm{e}}} $ = 200,
$ L_{{\rm{min}}} $ = 3, $ S_{{\rm{min}}} $ = 5
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表 2 居里温度预测: 四种机器学习模型的评价指标
Table 2. Curie temperature prediction: evaluation metrics for four machine learning models.
模型 MAE RMSE R2 RF 39.9912 82.2442 0.9175 GBDT 36.5400 79.1969 0.9235 XGB 33.5953 76.5587 0.9285 ET 40.7323 81.8218 0.9184
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