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001053929 0247_ $$2datacite_doi$$a10.34734/FZJ-2026-01614
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001053929 1001_ $$0P:(DE-Juel1)186072$$aWasmer, Johannes$$b0$$eCorresponding author$$ufzj
001053929 1112_ $$aFZJ IAS Retreat 2025$$cJülich$$d2025-05-27 - 2025-05-27$$wGermany
001053929 245__ $$aTowards all-electron treatment in electronic structure machine learning
001053929 260__ $$c2025
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001053929 502__ $$cRWTH Aachen
001053929 520__ $$aElectronic structure calculations with density functional theory (DFT) are today the most widely used technique for computational materials design. As materials design enters the artificial intelligence (AI) age, large databases of these calculations, such as the Materials Project, form the basis for creating foundation models. This can be seen in leaderboards such as Matbench Discovery [1]. These AI foundation models mark a paradigm shift in computational materials design since they accelerate the time to solution by orders of magnitude. However, the current DFT databases and AI surrogate models are not accurate enough to design quantum materials. This problem can be solved by employing all-electron, full-potential, fully relativistic DFT, that can reach the required calculation precision. The JuDFT.de codes are among the few established implementations of such methods [2]. They are the only all-electron DFT codes supporting high-throughput data generation that implement the FAIR data guidelines via their plugin integration with the AiiDA.net workflow engine.We present our progress on the open problem of surrogate AI models for all-electron DFT data for the JuDFT codes. We focus on the JuKKR code, and materials systems with defects, such as magnetically doped topological insulators [3]. We predict the converged electron potential for accelerated DFT, and first-principles exchange interactions for accelerated spin dynamics.[1] Riebesell, J. et al. Matbench Discovery – A framework to evaluate machine learning crystal stability predictions. Preprint at https://doi.org/10.48550/arXiv.2308.14920 (2024).[2] Bosoni, E. et al. How to verify the precision of density-functional-theory implementations via reproducible and universal workflows. Nat Rev Phys 6, 45–58 (2024).[3] Mozumder, R., Wasmer, J., Antognini Silva, D., Blügel, S. & Rüßmann, P. High-throughput magnetic co-doping and design of exchange interactions in topological insulators. Phys. Rev. Mater. 8, 104201 (2024).
001053929 536__ $$0G:(DE-HGF)POF4-5211$$a5211 - Topological Matter (POF4-521)$$cPOF4-521$$fPOF IV$$x0
001053929 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
001053929 536__ $$0G:(DE-Juel-1)aidas_20200731$$aAIDAS - Joint Virtual Laboratory for AI, Data Analytics and Scalable Simulation (aidas_20200731)$$caidas_20200731$$x2
001053929 536__ $$0G:(GEPRIS)491111487$$aDFG project G:(GEPRIS)491111487 - Open-Access-Publikationskosten / 2025 - 2027 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x3
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001053929 65027 $$0V:(DE-MLZ)SciArea-170$$2V:(DE-HGF)$$aMagnetism$$x1
001053929 65017 $$0V:(DE-MLZ)GC-1604-2016$$2V:(DE-HGF)$$aMagnetic Materials$$x0
001053929 7001_ $$0P:(DE-Juel1)157882$$aRüssmann, Philipp$$b1$$eCollaboration author$$ufzj
001053929 7001_ $$0P:(DE-HGF)0$$aSanvito, Stefano$$b2$$eCollaboration author
001053929 7001_ $$0P:(DE-HGF)0$$aCangi, Attila$$b3$$eCollaboration author
001053929 7001_ $$0P:(DE-Juel1)188313$$aAssent, Ira$$b4$$eCollaboration author$$ufzj
001053929 7001_ $$0P:(DE-Juel1)130548$$aBlügel, Stefan$$b5$$eThesis advisor$$ufzj
001053929 8564_ $$uhttps://iffgit.fz-juelich.de/phd-project-wasmer/presentations/2025-05-27-poster-ias-retreat
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