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Author Cioni, M.; Delle Piane, M.; Polino, D.; Rapetti, D.; Crippa, M.; Arslan Irmak, E.; Van Aert, S.; Bals, S.; Pavan, G.M. url  doi
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  Title Sampling real-time atomic dynamics in metal nanoparticles by combining experiments, simulations, and machine learning Type A1 Journal article
  Year (down) 2024 Publication Advanced Science Abbreviated Journal  
  Volume Issue Pages 1-13  
  Keywords A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)  
  Abstract Even 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. Experimental and computational techniques are bridged to unveil atomic dynamics in gold nanoparticles (NPs), using annular dark-field scanning transmission electron microscopy and molecular dynamics simulations informed by machine learning. The approach provides unprecedented insights into the real-time structural behaviors of NPs, merging state-of-the-art techniques to accurately characterize their dynamics under realistic conditions. image  
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  Language Wos https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=brocade2&SrcAuth=WosAPI&KeyUT=WOS:001206 Publication Date 2024-04-24  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2198-3844 ISBN Additional Links UA library record; WoS full record; WoS full record  
  Impact Factor 15.1 Times cited Open Access OpenAccess  
  Notes This work was supported by the funding received by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 818776- DYNAPOL, no. 770887 PICOMETRICS and no. 815128 REALNANO). The authors also acknowledge the computational resources provided by the Swiss National Supercomputing Center (CSCS), by CINECA, and the Research Foundation Flanders (FWO, Belgium) G.0346.21N. Approved Most recent IF: 15.1; 2024 IF: 9.034  
  Call Number UA @ admin @ c:irua:205442 Serial 9171  
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