Machine Learning Predictions of High-Curie-Temperature Materials

Abstract

Technologies that function at room temperature often require magnets with a high Curie temperature, $T_{\mathrm{C}}$, and can be improved with better materials. Discovering magnetic materials with a substantial $T_{\mathrm{C}}$ is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known data sets of experimental Curie temperatures, we develop machine-learning models to make rapid $T_{\mathrm{C}}$ predictions solely based on the chemical composition of a material. We train a random forest model and a $k$-NN one and predict on an initial dataset of over 2,500 materials and then validate the model on a new dataset containing over 3,000 entries. The accuracy is compared for multiple compounds' representations ("descriptors") and regression approaches. A random forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. A random forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-$T_{\mathrm{C}}$ materials and under-predict high-$T_{\mathrm{C}}$ materials. For exhaustive searches to find new high-$T_{\mathrm{C}}$ materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.

arXiv comment9 pages, 11 figures, accepted to Applied Physics Letters, special issue "Accelerate Materials Discovery and Phenomena"

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Cited by in corpus (3)

• The Northeast Materials Database for Magnetic Materials
• Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures
• A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks