Machine Learning algorithms for optimization of magnetocaloric effect in all-d-metal Heusler alloys

TL;DR

This paper demonstrates how machine learning, combined with genetic algorithms, can effectively optimize magnetocaloric effects in all-d-metal Heusler alloys, leading to the discovery of promising new alloy compositions.

Contribution

It introduces a novel integrated approach using Random Forest regression and genetic algorithms to predict and optimize magnetocaloric properties in Heusler alloys.

Findings

High accuracy in structural property prediction

Identification of an alloy with maximum magnetization difference

Validation consistent with previous DFT studies

Abstract

This study examines the application of machine learning algorithms, specifically the Random Forest regression model, to optimize the magnetocaloric effect in all-d-metal Heusler alloys. The model was trained using descriptors related to the mean properties of individual atoms, the properties of simple compounds in their ground state, and measures of chemical disorder. It demonstrated high accuracy in predicting structural properties, while exhibiting moderate accuracy in predicting magnetic properties. To identify optimal alloy compositions, a genetic algorithm was used to find those with the greatest differences in magnetization during martensitic transitions. Using this combined approach, the Ni-Co-Mn-Ti alloy system was thoroughly explored, resulting in the discovery of an alloy with a maximum magnetization difference. These results are consistent with previous research based on Density Functional Theory (DFT) and highlight the effectiveness of integrating machine learning with genetic algorithms for the discovery of new materials with outstanding magnetocaloric properties. The study emphasizes the need for further refinement of models capable of accurately predicting complex magnetic interactions, which is essential for fully leveraging the potential of all-d-metal Heusler alloys in practical applications.