Ab-initio based models for temperature-dependent magneto-chemical interplay in bcc Fe-Mn alloys

TL;DR

This paper develops ab-initio based models incorporating atomic spin and chemical variables to predict temperature-dependent magnetic and thermodynamic properties of bcc Fe-Mn alloys, including effects of vacancies and alloy composition.

Contribution

It introduces effective interaction models fitted to DFT data using knowledge-driven and machine-learning approaches, enabling detailed simulations of magnetic and thermodynamic behaviors.

Findings

Curie temperature decreases with Mn concentration

Temperature evolution of mixing enthalpy correlates with magnetization

Binding free energy between vacancy and Mn atom quantified

Abstract

Body-centered cubic (bcc) Fe-Mn systems are known to exhibit a complex and atypical magnetic behaviour from both experiments and 0 K electronic-structure calculations, which is due to the half-filled 3d-band of Mn. We propose effective interaction models for these alloys, which contain both atomic spin and chemical variables. They were parameterized on a set of key density functional theory (DFT) data, with the inclusion of non-collinear magnetic configurations being indispensable. Two distinct approaches, namely a knowledge-driven and a machine-learning approach have been employed for the fitting. Employing these models in atomic Monte Carlo simulations enables the prediction of magnetic and thermodynamic properties of the Fe-Mn alloys, and their coupling, as functions of temperature. This includes the decrease of Curie temperature with increasing Mn concentration, the temperature evolution of the mixing enthalpy and its correlation with the alloy magnetization. Also, going beyond the defect-free systems, we determined the binding free energy between a vacancy and a Mn atom, which is a key parameter controlling the atomic transport in Fe-Mn alloys.