On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics.

ABSTRACT: The large compositional space of high entropy alloys (HEA) often presents significant challenges in comprehensively deducing the critical influence of atomic composition on their mechanical responses. We propose an efficient nonparametric kernel-based probabilistic computational mapping to obtain the optimal composition of HEAs under ballistic conditions by exploiting the emerging capabilities of machine learning (ML) coupled with molecular-level simulations. Compared to conventional ML models, the present Gaussian approach is a Bayesian paradigm that can have several advantages, including small training datasets concerning computationally intensive simulations and the ability to provide uncertainty measurements of molecular dynamics simulations therein. The data-driven analysis reveals th

SUBMITTER: Gupta KK 

PROVIDER: S-EPMC11263686 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature