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Discovering compact, stable, and easily controllable nanoscale non-trivial topological magnetic structures, such as magnetic skyrmions, is the key to developing next-generation high-density, high-speed, and low-energy non-volatile information storage devices. Based on the topological generation mechanism, magnetic skyrmions can be generated through the Dzyaloshinskii–Moriya interaction (DMI) caused by breaking space-reversal symmetry. Two-dimensional (2D) non-centrosymmetric Janus structurecan generate vertical built-in electric fields to break spatial inversion symmetry. Therefore, seeking for 2D Janus material with intrinsic magnetism is fundamental to develop the novel chiral magnetic storage technologies. In this work, we combine detailed machine learning techniques and first-principle calculations to investigate the magnetism of the unexplored 2D Janus material. We first collect 1179 2D hexagonal ABC-type Janus materials based on the Materials Project database, and use elemental composition as feature descriptors to construct four machine learning models: random forest (RF), gradient boosting decision trees (GBDT), extreme gradient boosting (XGB), and extra trees (ET). These algorithms and models are constructed to predict lattice constants, formation energy, and magnetic moment, via hyperparameter optimization and ten-fold cross-validation. The GBDT exhibits the highest accuracy and best prediction performance for magnetic moment classification. Subsequently, the collected data of 82018 yet-undiscovered 2D Janus materials, are input into the trained models to generate 4024 high magnetic moment 2D Janus materials with thermal stability. First-principles calculations are employed to validate random sample of 13 Janus materials with high magnetic moment. This study provides an effective machine learning framework for classifying the magnetic moments and screening highthroughput 2D Janus structures, thereby accelerating the exploration of their magnetic properties.
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Keywords:
- machine learning /
- two-dimensional Janus materials /
- magnetic moment /
- first-principles calculations
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模型 超参数 GBDT(磁矩分类) learning_rate = 0.01603011, max_depth = 5, n_estimators = 272, subsample = 0.69895067 GBDT(形成能) learning_rate = 0.02, max_depth = 6, n_estimators = 353, subsample = 0.93030056 ET(晶格常数a和b) max_depth = 10, max_features = 0.60, n_estimators = 100,
min_samples_leaf = 2, min_samples_split = 4XGB(晶格常数c) learning_rate = 0.02, n_estimators = 300, max_depth = 5,
subsample = 0.8, colsample_bytree = 0.49613519
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模型 Lattice a = b Lattice c MAE RMSE $R^2$ MAE RMSE $R^2$ RF 0.5485 0.8104 0.7375 0.6491 1.0001 0.6872 GBDT 0.4477 0.7350 0.7829 0.6679 0.9924 0.6923 XGB 0.5427 0.7968 0.7462 0.5953 0.9474 0.7186 ET 0.3469 0.6808 0.8137 0.6534 1.0103 0.6817
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模型 MAE RMSE $R^2$ RF 0.1054 0.1697 0.8671 GBDT 0.0798 0.1411 0.9070 XGB 0.0959 0.1533 0.8930 ET 0.1120 0.1701 0.8657
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模型 Accuracy Precision Recall F1 score RF 0.8770 0.8459 0.7636 0.7862 GBDT 0.8948 0.8498 0.8182 0.8263 XGB 0.8762 0.8398 0.7697 0.7883 ET 0.8795 0.8392 0.7778 0.7965
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Formula Lattice constants Formation energy/eV $ |\mu| / \mu_{\mathrm{B}} $ a = b/Å c/Å A B C ErFeTb 3.35 18.25 –2.02 2.51 3.03 6.24 FeNO 2.92 15.00 –11.87 1.17 0.08 0.47 HoRuSr 4.90 18.79 –6.66 3.79 0.02 0.05 DyOsSr 4.18 18.87 –6.89 4.89 0 0.13 EuSbSr 5.43 18.69 –5.53 6.85 0.01 0.05 HoIrSr 4.58 18.79 –7.24 3.72 0 0.05 LiUZn 2.89 18.13 –0.44 0 1.65 0.01 PuSZn 4.52 18.13 –6.75 5.61 0.10 0.01 GdKU 7.46 18.13 –2.39 7.33 0 2.96 LuNbTi 3.02 18.13 –1.76 0.02 0.28 1.67 GdHfSe 5.03 18.93 –8.46 7.33 0.34 0.02 NaTbZn 4.65 18.69 –1.87 0.02 6.00 0 HoNpSr 3.69 18.46 –1.80 3.81 4.38 0.08
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[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] -
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