Machine Learning of Atomic Dynamics and Statistical Surface Identities in Gold Nanoparticles

04 November 2022, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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. Here we show a machine learning approach that allows us to reconstruct the complex atomic dynamics of metal NPs from high-dimensional data extracted 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. Tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a “statistical equivalent identity” for metal NPs based on the intrinsic atomic dynamics present within them.


Gold nanoparticles
Machine learning
Atomic environments
Atomic dynamics
Statistical identities


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.