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Machine Learning (ML) has impacted numerous areas of materials science, mostprominently improving molecular simulations, where force fields were trained onpreviously relaxed structures. One natural next step is to predict materialproperties beyond structure. In this work, we investigate the applicability andexplainability of ML methods in the use case of estimating the criticaltemperature for magnetic Heusler alloys calculated using ab initio methodsdetermined materials-specific magnetic interactions and a subsequent MonteCarlo (MC) approach. We compare the performance of regression andclassification models to predict the range of the critical temperature of givencompounds without performing the MC calculations. Since the MC calculationrequires computational resources in the same order of magnitude as thedensity-functional theory (DFT) calculation, it would be advantageous toreplace either step with a less computationally intensive method such as ML. Wediscuss the necessity to generate the magnetic ab initio results to make aquantitative prediction of the critical temperature. We used state-of-the-artexplainable artificial intelligence (XAI) methods to extract physical relationsand deepen our understanding of patterns learned by our models from theexamined data.

Machine Learning-based estimation and explainable artificial  intelligence-supported interpretation of the critical temperature from  magnetic ab initio Heusler alloys data