This study decodes the intricate atomic dynamics of metal nanoparticles (NPs) using a machine learning approach. Analyzing high-dimensional data from molecular dynamics simulations of gold NPs with different shapes, an atomic environments (AEs) dictionary labels individual atoms, identifying native and non-native AEs. Tracking AE emergence, annihilation, lifetime, and interconversion provides a 'statistical equivalent identity' for the NPs, offering a comprehensive view of the atomic dynamics influencing their properties.
Publisher
Communications Chemistry
Published On
Jul 05, 2023
Authors
Daniele Rapetti, Massimo Delle Piane, Matteo Cioni, Daniela Polino, Riccardo Ferrando, Giovanni M. Pavan
Tags
metal nanoparticles
atomic dynamics
machine learning
molecular dynamics
gold NPs
atomic environments
statistical equivalent identity
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