Predicting the Curie temperature of magnetic materials across chemistries and structures

We present a technique [1] for predicting the Curie temperature of magnetic materials using density functional theory (DFT) calculations suitable to include in high-throughput frameworks. As predictive factors, the model considers the energy difference between the magnetic ground state and a magnetically disordered paramagnetic state, the magnetic entropy of the paramagnetic state, and the number of nearest magnetic neighbors of the magnetic atoms. The model is tested on disordered alloy systems, it successfully reproduces the concentration-dependent trends of the Curie temperature, in qualitative agreement with experimental data. 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 DFT 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, serving as a complementary tool alongside Heisenberg Hamiltonian-based Monte Carlo simulations, DFT calculations, or machine-learning-driven screening methods.



[1] M. A. Brännvall, G. Persson, L. Casillas-Trujillo, R. Armiento, and B. Alling, arXiv preprint arXiv:2406.02084, 2024.