An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature

Figures & data

Figure 1. Distribution of T_C. (a) Histogram of T_C for 1749 FM materials in the database. (b) Distribution of the elements for compounds in the FM database.

Figure 2. Performance of the classification model. (a): Confusion matrix of FM and AFM classification test set (b): Line graph represents the accuracy, precision, recall and F₁ score of the 10-folder cross validation of classification data, and the pie chart denotes the most important features in classification model (cf. Supplementary for detailed explanation).

Figure 3. Regression model performance and feature importance. (a) Predicted vs. experimental T_C for test set. (b) Line graph represents the MAE and R² score of the 10-folder cross validation of our database, and the pie chart denotes the most important features in two-step regression model (cf. Supplementary for detailed explanation).

Figure 4. T_C prediction. (a) Histogram of predicted T_C for 3002 FM materials in the database. (b) Scatter plot of predicted vs. experimental T_C for 85 compounds with experimental T_C.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.