Application of batch learning for boosting high-throughput <i>ab initio</i> success rates and reducing computational effort required using data-driven processes

Abstract The increased availability of computing time, in recent years, allows for systematic high-throughput studies of material classes. Such studies serve 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 (ML)-based approach to improve the input parameters optimized during a density functional theory computation or workflow. This approach of integrating ML into a typical high-throughput workflow demonstrates the advantages and necessary considerations for a systematic study of magnetic multilayers of 3 d 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.