Spectra-based clustering of high-entropy alloy catalysts: improved insight over use of atomic structure.

ABSTRACT: The investigation of material properties based on atomic structure is a commonly used approach. However, in the study of complex systems such as high-entropy alloys, atomic structure not only covers an excessively vast chemical space, but also has an imprecise correspondence to chemical properties. Herein, we present a label-free machine learning (ML) model based on physics-based spectroscopic descriptors to study the catalytic properties of AgAuCuPdPt high-entropy alloy catalysts. Even if the atomic structures of two such alloys are different, these alloys may have similar catalytic properties if their spectral characteristics match closely. One cluster with the strongest CO adsorption exhibited high selectivity for C₂₊ product generation, indicating that the spectra-based ML m

SUBMITTER: Li H 

PROVIDER: S-EPMC11808395 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature