Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning

Author:

Cioni Matteo1ORCID,Delle Piane Massimo1ORCID,Polino Daniela2ORCID,Rapetti Daniele1ORCID,Crippa Martina1ORCID,Irmak Ece Arslan3ORCID,Van Aert Sandra3ORCID,Bals Sara3ORCID,Pavan Giovanni M.12ORCID

Affiliation:

1. Department of Applied Science and Technology Politecnico di Torino Corso Duca degli Abruzzi 24 Torino 10129 Italy

2. Department of Innovative Technologies University of Applied Sciences and Arts of Southern Switzerland Polo Universitario Lugano Campus Est, Via la Santa 1 Lugano‐Viganello 6962 Switzerland

3. EMAT and NANOlab Center of Excellence University of Antwerp Groenenborgerlaan 171 Antwerp 2020 Belgium

Abstract

AbstractEven at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic‐resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state‐of‐the‐art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark‐field scanning transmission electron microscopy enables the acquisition of ten high‐resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real‐time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.

Funder

Fonds Wetenschappelijk Onderzoek

European Research Council

Publisher

Wiley

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