Curie Temperature Prediction Models of Magnetic Heusler Alloys Using Machine Learning Methods Based on First-Principles Data From Ab-initio KKR-GF Calculations

Hilgers, Robin; Kovacik, Roman; Blügel, Stefan; Wortmann, Daniel

Conference Presentation (After Call)

FZJ-2023-01453

Curie Temperature Prediction Models of Magnetic Heusler Alloys Using Machine Learning Methods Based on First-Principles Data From Ab-initio KKR-GF Calculations

Hilgers, R. (Corresponding author)FZJ*RWTH* ; Wortmann, D.FZJ* ; Blügel, S.FZJ* ; Kovacik, R.FZJ*

2023

APS March Meeting, Las Vegas, Nevada, USA, 6 Mar 2023 - 10 Mar 2023

Please use a persistent id in citations: http://hdl.handle.net/2128/34323

Abstract: Ordered and disordered magnetic Heusler alloys are an important class of materials in science and applications. Using Curie temperatures (Tc) of Heusler alloys calculated by the Korringa-Kohn-Rostoker Green function (KKR-GF) method and a subsequent Monte Carlo (MC) approach [1], we trained and evaluated several machine learning models to predict Tc based on atomic, magnetic, and structural properties. We studied multiple descriptor selection methods to determine the most meaningful physical quantities in the given phase space.We compared the performance of regression and classification models in order to predict the range of the Tc of given compounds without performing the MC calculations. Since the MC calculation takes about as many computational resources as the ab-initio calculation, it would be favorable to replace either step with a less computational intensive method as e.g. machine learning. We discuss the necessity to generate the magnetic ab-initio results in order to make a quantitative prediction of the Tc.This work can be seen as a small-scale case study in which lightweight machine learning algorithms can add value to existing ab-initio data and eventually replace costly computational steps in layered calculation workflows in the future.[1] R. Kovacik et al. (2022), [10.24435/MATERIALSCLOUD:WW-PV]*This work was performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) and received funding from the Helmholtz Association of German Research Centres.

Keyword(s): Magnetic Materials (1st)

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