guest ::
| Home > Publications database > Application of batch learning for boosting high-throughput ab initio success rates and reducing computational effort required using data-driven processes |
| Home > Publications database > Application of batch learning for boosting high-throughput ab initio success rates and reducing computational effort required using data-driven processes |
| Preprint | FZJ-2023-04921 |
; ;
2023
arXiv
This record in other databases:
Please use a persistent id in citations: doi:10.34734/FZJ-2023-04921
Abstract: The 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.
Keyword(s): Magnetic Materials (1st) ; Condensed Matter Physics (2nd) ; Materials Science (2nd) ; Magnetism (2nd)
|
The record appears in these collections: |
PDF
Fulltext