Data Driven Determination in Growth of Silver from Clusters to Nanoparticles and Bulk

The thresholds among atomic clusters, nanoparticles, and the bulk state have been ambiguous. A potential solution is to determine cluster growth toward bulk, but this is challenging to determine with experiments and computation. Data science is proposed to predict atomic cluster growth and determine the cluster-nanoparticle-bulk thresholds using Ag clusters as a prototype element. Supervised machine learning reveals that Ag cluster growth has nonlinear models where nonlinear machine learning is found to accurately predict binding energy. Unsupervised machine learning discovers three groups (cluster, semiclusters, and nanoparticles) where linear regression is used to predict the binding energy in each group. Furthermore, machine learning reveals the linear relationship between binding energy and the surface-to-volume ratio of Ag nanoparticles. This allows for a binding energy estimation of large Ag nanoparticles and a revelation of how Ag nanoparticles grow toward the bulk. Thus, data science is proposed as a powerful tool for determining cluster growth and thresholds for clusters, nanoparticles, and bulk states.