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二维Janus磁性体系因空间反演对称性破缺, 易产生较大的DMI作用项(Dzyaloshinskii Moriya interaction), 为探索磁拓扑结构及发展新一代赛道级磁存储器件提供了理想的平台. 在该研究领域中, 寻找具有高居里温度的体系至关重要, 这直接关系到材料在实际高温环境下的磁性能稳定性与应用潜力. 本研究基于已有文献报道与开源数据库, 构建了包含16880种ABC型二维材料的数据集. 以材料的化学计量比、元素固有属性及电子结构特征为描述符, 分别采用随机森林、梯度提升决策树、极端梯度提升和极端随机树四种机器学习模型, 对二维材料的居里温度展开预测训练, 并通过十折交叉验证评估模型的学习性能. 研究结果表明, 极端梯度提升模型对居里温度的预测精度最高且具备最优的泛化能力. 基于最优模型, 对4024种尚未探索的二维Janus结构材料进行了居里温度预测, 最终筛选出54种同时具备热稳定性、高磁矩、高居里温度的二维Janus材料. 为验证预测结果的可靠性, 研究进一步结合第一性原理计算与海森堡模型, 对随机选取的4种候选Janus体系进行了理论验证. 本研究为加速发掘兼具稳定性与高居里温度的二维Janus磁性材料提供了新的技术途径, 对推动二维磁性材料在信息存储等领域的应用具有重要意义.
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关键词:
- 机器学习 /
- 二维磁性Janus材料 /
- 居里温度 /
- 第一性原理计算
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.-
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
Fig. 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
Fig. 2. Heat map of curie temperature distribution and elemental abundance.
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
Fig. 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
Fig. 5. Magnetic moment versus temperature curves for four randomly selected janus materials.
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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