Modelling magnetic materials at finite temperature: Alloys, altermagnets, and interfacing with machine learning
Date:
Invited speaker for the Quantum Matter seminar in the School of Physics at the University of Bristol, UK.
Abstract
Accurately simulating magnetic materials remains a challenge for materials theorists. Magnetic moments represent an additional set of degrees of freedom which thermally disorder with increasing temperature. The resulting ensemble of magnetic configurations determines both a material’s underlying electronic structure and many consequent physical properties that experiments probe. These magnetic moments also couple to atomic degrees of freedom, meaning that a material’s magnetic state can influence its phase stability and vice versa. In this talk, from the perspective of first-principles electronic structure calculations and atomistic simulation, I will present a selection of results from recent investigations in this area. I will describe: (i) how magnetic order drives spinodal decomposition in ternary Cu–Ni–Fe alloys [1], (ii) how thermally induced spin fluctuations cause altermagnetic band splitting in CrSb and MnTe to collapse rapidly with increasing temperature [2], and (iii) how machine learning, specifically magnetism-aware machine-learned interatomic potentials, can accelerate otherwise computationally demanding atomic-scale modelling of the mechanical and defect properties of materials containing magnetic elements, such as austenitic stainless steels [3]. I will conclude by giving an outlook on the work I intend to conduct in my new position at Cambridge, where my Fellowship focuses on improving the treatment of magnetism in these machine-learning models.
References
[1] X. Zhang, C. D. Woodgate, et al., Acta Mater. 307, 121965 (2026).
[2] C. D. Woodgate et al., arXiv:2603.15035.
[3] L. Shenoy, C. D. Woodgate, et al., Phys. Rev. Mater. 8, 033804 (2024).