001008352 001__ 1008352
001008352 005__ 20230703203313.0
001008352 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-02298
001008352 037__ $$aFZJ-2023-02298
001008352 041__ $$aEnglish
001008352 1001_ $$0P:(DE-Juel1)179506$$aHilgers, Robin$$b0$$eCorresponding author$$ufzj
001008352 1112_ $$aHelmholtz.Ai$$cHamburg$$d2023-06-13 - 2023-06-14$$wGermany
001008352 245__ $$aApplication of Machine-Learning Models and XAI in Materials Science Using Magnetic DFT Data
001008352 260__ $$c2023
001008352 3367_ $$033$$2EndNote$$aConference Paper
001008352 3367_ $$2BibTeX$$aINPROCEEDINGS
001008352 3367_ $$2DRIVER$$aconferenceObject
001008352 3367_ $$2ORCID$$aCONFERENCE_POSTER
001008352 3367_ $$2DataCite$$aOutput Types/Conference Poster
001008352 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1688374050_3226$$xOther
001008352 520__ $$aThe prediction of material properties using ab-initio simulations [1] is a research field of fundamental significance due to the multitude of materials applications in technology and research. Such simulations can require huge computational resources if carried out for many materials, and thus the possibility to employ machine learning methods offers potential benefit by restricting the materials search space.In our work, we demonstrate this idea for the particular field of ordered and disordered magnetic Heusler alloys. In detail, we focus on theirCurie-temperature, which often is the major quantity describing the fitness of a magnetic material for application. Thus, we studied the possibility to predict the Curie-temperature and thereby classifying the materials relevance for possible technical application using machine-learning algorithms.We compared the performance of regression models and classification models in order to predict the range of the Curie-temperature of given compounds to demonstrate the possibility to reduce the computational expanses of simulating this quantity. This work can be seen as a small-scale case study in which lightweight machine learning algorithms can add value to existing simulation data and eventually replace costly computational steps in layered calculation workflows in the future. In addition, we demonstrate, that such models can lead to interesting unbiased physical insight. Acknowledgement: 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.[1] R.  Kovacik, P. Mavropoulos, S. Blüugel, Materials Cloud (2022) 10.24435/MATERIALSCLOUD:WW-PV
001008352 536__ $$0G:(DE-HGF)POF4-5211$$a5211 - Topological Matter (POF4-521)$$cPOF4-521$$fPOF IV$$x0
001008352 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x1
001008352 7001_ $$0P:(DE-Juel1)131042$$aWortmann, Daniel$$b1$$ufzj
001008352 7001_ $$0P:(DE-Juel1)130548$$aBlügel, Stefan$$b2$$ufzj
001008352 8564_ $$uhttps://juser.fz-juelich.de/record/1008352/files/Poster.pdf$$yOpenAccess
001008352 909CO $$ooai:juser.fz-juelich.de:1008352$$popenaire$$popen_access$$pVDB$$pdriver
001008352 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179506$$aForschungszentrum Jülich$$b0$$kFZJ
001008352 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131042$$aForschungszentrum Jülich$$b1$$kFZJ
001008352 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)130548$$aForschungszentrum Jülich$$b2$$kFZJ
001008352 9131_ $$0G:(DE-HGF)POF4-521$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5211$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Materials$$x0
001008352 9141_ $$y2023
001008352 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001008352 920__ $$lyes
001008352 9201_ $$0I:(DE-Juel1)IAS-1-20090406$$kIAS-1$$lQuanten-Theorie der Materialien$$x0
001008352 9201_ $$0I:(DE-Juel1)PGI-1-20110106$$kPGI-1$$lQuanten-Theorie der Materialien$$x1
001008352 980__ $$aposter
001008352 980__ $$aVDB
001008352 980__ $$aUNRESTRICTED
001008352 980__ $$aI:(DE-Juel1)IAS-1-20090406
001008352 980__ $$aI:(DE-Juel1)PGI-1-20110106
001008352 9801_ $$aFullTexts