Deep Potential Development of Highly Concentrated/High Entropy-driven Carbides

In this study, DeepMD (Deep Potential Molecular Dynamics) code was utilized to develop Deep potentials for highly concentrated/high entropy driven TM-rich carbides (TM= transition metals) as the key precipitates in Ni-based Superalloys. The deep learning algorithm has been trained against ab-initio molecular dynamics data generated in VASP following Density Functional Theory (DFT) approximations. The data sets include the energy, force, and virial of corresponding supplied trajectories and atoms. The accuracy of the Deep potentials was then tested using classical molecular dynamics simulations with the focus on the elastic and thermo-mechanical property validations. The use of high-entropy alloy (HEA) compositional strategy to maximize the interaction statistics within the carbide phases allowed us to construct the multi-component potentials. 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.