Simulation studies of the stability and growth kinetics of Pt-Sn phases using a machine learning interatomic potential

Abstract

The thermodynamic stability and growth kinetics of Pt-Sn phases are investigated by atomistic simulations utilizing a neural-network machine learning (NN-ML) interatomic potential. The physical properties of Pt-Sn crystalline phases described by the NN-ML interatomic potential, such as equation of states, formation energy convex hull, and phonon vibrational spectrum, are in in accord well with first-principles calculations and experimental data. The calculations of temperature dependent Gibbs free energies of the crystalline Pt-Sn phases by the NN-ML potential are in the efficiency of empirical interatomic potentials and accuracy of density functional theory (DFT). The developed NN-ML potential is also used to investigate the structures and dynamics of liquid phases of Pt-Sn alloys by molecular dynamics (MD) simulations. The crystallization of PtSn and Pt3Sn phases from the solid–liquid interface are also studied by MD simulations using the NN-ML potential. The results obtained from our studies provide useful insight into thermodynamics stability and growth kinetics of Pt-Sn binary phases.