Machine learning strategies for potential development in highly concentrated/high-entropy driven Ni-based Superalloys

In this current work, we developed the Deep Learning potentials for highly concentrated multi-component metallic system by utilizing the DeepMD as the Deep Learning/Machine Learning code.  For this purpose, we employed the outcome from the ab-initio molecular dynamics simulations from the Vienna Ab-initio Software Package (VASP) following the Density Functional Theory (DFT) approximations including the data of energy, forces, and virial database. These data were strategically sampled from compositions close to the concentrated system and/or high-entropy alloy with the same constituents. The efficiency and validity of the developed potentials were verified through a series of predictive molecular dynamics simulations of thermomechanical properties that are of great interest to the development of advanced Ni-based Superalloys. The support from the National Energy Technology Laboratory (Grant No. FE0031554) is gratefully acknowledged. We would also like to express our gratitude to NERSC for providing the supercomputer resource.