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Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A potential energy surface (PES) for describing the interactions among the atoms in Ag nanoparticles was derived using the feedforward artificial neural network (ANN) method. Based on the preliminary success of constructing ANN PESs using a small number of data sets for Pt, Au, and Ag clusters/nanoparticles, we studied here the accuracy of the ANN method to build the PES for Ag nanoparticles to be employed in molecular dynamics (MD) simulations by including more data sets obtained from density functional theory (DFT) calculations. In this work, more neurons were used to improve the fitting accuracy. The results demonstrated that the new fitting provides a more balanced result in terms of accuracy in training and testing with respect to the previously fitting, however, more asymptotic DFT data sets are required to construct a global ANN PES suitable for MD simulations on the formation of Ag nanoparticles.