Structural Chart of Copper-Silver Nanoalloys through machine learning

Abstract

Nanoalloys (or alloy nanoparticles) are an important class of materials that are promising for their functional properties. However, designing synthesis protocols to control their structure and chemical ordering is rather challenging. Part of this difficulty stems from the lack of information on their metastable and stable structures. Here, we develop a general computational framework to construct a structural chart of nanoalloys using 38-atom AgCu nanoalloys as a model system. Initially, the equilibrium structural distribution is sampled using parallel tempering combined with molecular dynamics (PTMD). Using a machine learning (ML) based approach, the vast number of sampled configurations are classified into various structural classes. This ML approach produces a single three-dimensional map in which all structures and compositions can be visualized and discriminated. Finally, a finite-temperature structural chart is constructed which provides information on the dominant structures across the entire range of compositions and temperatures. In addition, the structural chart reveals significant differences in thermal stability between nanoalloys and bulk alloys. The presented framework provides an effective route to compute and map the vast structural and chemical space of multicomponent nanoparticles, paving the way to the rational design of functional nanoalloys.

Figures (11)

• Figure 1: Excess energy ($\Delta$) of global minima. The data points are color-coded according to their structure type as better described in the main text. Representative structures are reported on the right hand side, with the following compositions: fcc (Ag$_{38}$), Ih (aM) (Ag$_{37}$Cu$_{1}$), pIh$_6$ (Ag$_{32}$Cu$_{6}$), 7-atom fcc core, chiral shell (Ag$_{31}$Cu$_{7}$), and pIh (Ag$_{30}$Cu$_{8}$).
• Figure 2: ISV space showing (a) composition map, (b) KMeans clustering with 150 clusters, and (c) final 10 broad structural classes. The color gradient in (a) from black to red indicates alloy composition from pure Cu to pure Ag.
• Figure 3: Fcc-based motifs: (a) perfect fcc, (b) 6-atom fcc core and the 28-atom chiral shell in a 34-atom mix structure, (c) 7-atom fcc core and the 31-atom chiral shell in a 38-atom mix structure, (d) 6-atom fcc core with a dense 32-atom shell.
• Figure 4: Ih-based motifs. (a) 39-atom structure (in red) carved out from (left) 127-atom anti-Mackay icosahedron and (right) 55-atom Mackay icosahedron. (b) Ih aM (anti-Mackay icosahedron), (c) Ih chiral (chiral icosahedron, shown from both sides), (d) Ag$_{32}$Cu$_{6}$ pIh$_{6}$ (poly-icosahedron with 6 Cu atoms having icosahedral coordination) (e) pIh (poly-Icosahedron, atoms having icosahedral and undefined coordination are shown in yellow and white, respectively) (f) defective pIh (g) defective Ih (aM/chiral).
• Figure 5: (a) Structural chart of Ag$_{38-n}$Cu$_{n}$ nanoalloys as a function of composition and temperature. The same structural classes identified in Figure \ref{['fgr:isv']} are used.The black arrow points from the perfect core-shell arrangement to the composition where the highest thermal stability is observed. (b) Plot of Shannon entropy (the color gradient from black to cyan corresponds to values in the range 0 to 1.76).