ArticleCommunications chemistry2023
Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles.
Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- A Dynamic Silver(I) Nanocluster Holds Together a 3 × 3 Self-Assembled Grid.Journal of the American Chemical Society · 2025Article
- Relevant, Hidden, and Frustrated Information in High-Dimensional Analyses of Complex Dynamical Systems with Internal Noise.Journal of chemical theory and computation · 2025Article
- Classification and spatiotemporal correlation of dominant fluctuations in complex dynamical systems.PNAS nexus · 2025Article
- Sampling Real-Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Dynamic sampling of liquid metal structures for theoretical studies on catalysis.Chemical science · 2023Article
- Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs' properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. In this study, we decode the intricate atomic dynamics of metal NPs by using a machine learning approach analyzing high-dimensional data obtained from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs' dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. By tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a "statistical equivalent identity" for metal NPs, providing a comprehensive picture of the intrinsic atomic dynamics that shape their properties.
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Registered trials
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