  Machine Learning (ML) has impacted numerous areas of materials science, most
prominently improving molecular simulations, where force fields were trained on
previously relaxed structures. One natural next step is to predict material
properties beyond structure. In this work, we investigate the applicability and
explainability of ML methods in the use case of estimating the critical
temperature for magnetic Heusler alloys calculated using ab initio methods
determined materials-specific magnetic interactions and a subsequent Monte
Carlo (MC) approach. We compare the performance of regression and
classification models to predict the range of the critical temperature of given
compounds without performing the MC calculations. Since the MC calculation
requires computational resources in the same order of magnitude as the
density-functional theory (DFT) calculation, it would be advantageous to
replace either step with a less computationally intensive method such as ML. We
discuss the necessity to generate the magnetic ab initio results to make a
quantitative prediction of the critical temperature. We used state-of-the-art
explainable artificial intelligence (XAI) methods to extract physical relations
and deepen our understanding of patterns learned by our models from the
examined data.
