Rapid Atomic Structure Prediction of Multimetallic Nanoparticles with Physics-Based Machine Learning.

ABSTRACT: Metal nanoparticles (NPs) find tremendous application in various fields, including catalysis, biomedicine, and electronics, due to their unique physicochemical properties arising from their morphology (i.e., size and shape) and composition. The chemical ordering of NPs, consisting of more than one metal, is crucial for optimizing their application performance, including stability. Traditionally, Density Functional Theory (DFT) has been used to investigate NP stability, but it is computationally expensive, limited to small systems, and cannot be applied to multimetallic NPs, which have enormous materials space. To address this limitation, recent efforts coupled a physics-based model (Bond-Centric Model) with a developed genetic algorithm to optimize the chemical ordering of NPs, leading to

SUBMITTER: Alkhatib B 

PROVIDER: S-EPMC12290662 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature