Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures

We develop a technique for predicting the Curie temperature of magnetic materials using density functional theory calculations suitable to include in high-throughput frameworks. We apply four different models, including physically relevant observables, and assess numerical constants by studying 32 ferro- and ferrimagnets. With the best-performing model, the Curie temperature can be predicted with a mean absolute error of approximately 126 K. As predictive factors, the models consider either the energy differences between the magnetic ground state and a magnetically disordered paramagnetic state, or the average constraining fields acting on magnetic moments in a disordered local moments calculation. Additionally, the energy differences are refined by incorporating the magnetic entropy of the paramagnetic state and the number of nearest magnetic neighbors of the magnetic atoms. The most advanced model is found to extend well into <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:msub><a:mi>Fe</a:mi><a:mrow><a:mn>1</a:mn><a:mtext>−</a:mtext><a:mi>x</a:mi></a:mrow></a:msub><a:msub><a:mi>Co</a:mi><a:mi>x</a:mi></a:msub></a:mrow></a:math> alloys, indicating the potential efficacy of utilizing our model in designing materials with tailored Curie temperatures by altering alloy compositions. This examination can illuminate the factors influencing magnetic transition temperatures in magnetic materials and provide insights into how they can be employed to make quantitative predictions of Curie temperatures. Our approach is not restricted to specific crystal structures or chemical compositions. It offers a more cost-effective alternative, in terms of human time and need for hands-on oversight, to other density functional theory methods for predicting the Curie temperature. As a result, it provides a practical strategy for conducting high-throughput screening for new technologically applicable magnetic materials. Alternatively, it can complement ML-based screening of magnetic materials by integrating physical principles into such approaches, thereby enhancing their prediction accuracy. Published by the American Physical Society 2024