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001041738 1001_ $$0P:(DE-Juel1)179506$$aHilgers, Robin$$b0$$eCorresponding author
001041738 245__ $$aMachine Learning-based estimation and explainable artificial intelligence-supported interpretation of the critical temperature from magnetic ab initio Heusler alloys data
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001041738 520__ $$aMachine 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 (
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001041738 7001_ $$0P:(DE-Juel1)131042$$aWortmann, Daniel$$b1
001041738 7001_ $$0P:(DE-Juel1)130548$$aBlügel, Stefan$$b2
001041738 773__ $$0PERI:(DE-600)2898355-5$$a10.1103/PhysRevMaterials.9.044412$$gVol. 9, no. 4, p. 044412$$n4$$p044412$$tPhysical review materials$$v9$$x2475-9953$$y2025
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