Computational Exploration of Ni-P Bulk Metallic Glasses via Machine Learning Potentials

Bulk metallic glasses (BMGs) are a unique class of materials characterized by their disordered atomic structure, which imparts exceptional mechanical strength, corrosion resistance, and catalytic activity. In this study, we develop a machine learning interatomic potential (MLIP) for Ni-P-based BMGs using an artificial neural network approach. A comprehensive dataset spanning a broad range of temperatures, pressures, and compositions was generated through atomistic simulations. The MLIP was validated against density functional theory calculations, demonstrating high accuracy in predicting energy and forces. The developed potential allowed us to investigate the local atomic structure and dynamic behavior of Ni-P BMGs across different Ni-P ratios and temperature regimes. Our findings reveal the role of short- and medium-range ordering in the structural stability of these BMGs, highlighting the influence of phosphorus concentration on glass-forming ability. Additionally, we explored the upper compositional limit of P that can sustain the amorphous nature of Ni-P BMGs. The results provide critical insights into the atomic arrangement for the formation and stability of BMGs.