Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys.

ABSTRACT: The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing mod

SUBMITTER: Johnson NS 

PROVIDER: S-EPMC11355945 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature