Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles

• Published in: Communications chemistry Vol. 6; no. 1; p. 143
• Main Authors: Rapetti, Daniele, Delle Piane, Massimo, Cioni, Matteo, Polino, Daniela, Ferrando, Riccardo, Pavan, Giovanni M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05-07-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1034; 639/301/119; 639/638/440/94; 639/638/563/982; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Dimensional analysis; Gold; Low temperature; Machine learning; Molecular dynamics; Nanoparticles; Temperature dependence
• DOI: 10.1038/s42004-023-00936-z
• EISSN: 2399-3669

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.
Metal nanoparticles are effective catalysts with properties very different from their bulk species, but insight into atom dynamics within nanoparticles is non-trivial to attain. Here, a data-driven approach elucidates the physical mechanisms underlying the temperature-dependent properties of gold nanoparticles.