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001018603 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-04921
001018603 037__ $$aFZJ-2023-04921
001018603 041__ $$aEnglish
001018603 1001_ $$0P:(DE-Juel1)179506$$aHilgers, Robin$$b0$$eCorresponding author$$ufzj
001018603 245__ $$aApplication of batch learning for boosting high-throughput ab initio success rates and reducing computational effort required using data-driven processes
001018603 260__ $$barXiv$$c2023
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001018603 520__ $$aThe increased availability of computing time, in recent years, allows for systematic high-throughput studies of material classes with the purpose of both screening for materials with remarkable properties and understanding how structural configuration and material composition affect macroscopic attributes manifestation. However, when conducting systematic high-throughput studies, the individual ab initio calculations' success depends on the quality of the chosen input quantities. On a large scale, improving input parameters by trial and error is neither efficient nor systematic. We present a systematic, high-throughput compatible, and machine learning-based approach to improve the input parameters optimized during a DFT computation or workflow. This approach of integrating machine learning into a typical high-throughput workflow demonstrates the advantages and necessary considerations for a systematic study of magnetic multilayers of 3d transition metal layers on FCC noble metal substrates. For 6660 film systems, we were able to improve the overall success rate of our high-throughput FLAPW-based structural relaxations from 64.8% to 94.3 % while at the same time requiring 17 % less computational time for each successful relaxation.
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001018603 65027 $$0V:(DE-MLZ)SciArea-170$$2V:(DE-HGF)$$aMagnetism$$x2
001018603 65017 $$0V:(DE-MLZ)GC-1604-2016$$2V:(DE-HGF)$$aMagnetic Materials$$x0
001018603 7001_ $$0P:(DE-Juel1)131042$$aWortmann, Daniel$$b1$$ufzj
001018603 7001_ $$0P:(DE-Juel1)130548$$aBlügel, Stefan$$b2$$ufzj
001018603 8564_ $$uhttps://arxiv.org/abs/2311.15430
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