Development strategies and hyperparameter optimization of Deep Learning potentials for multi-component and multi-phase Nickel-based Superalloys

In this study, we developed Deep Learning interatomic potentials to model a multi-phase and multi-components system of Ni-based Superalloys. The complex system has up to ten elements with three major phase constituents, namely Gamma, Gamma Prime, and Transition-metal rich Carbide. We utilized invariant scalar-based and/or equivariant, tensor-based neural network (NN) approach as implemented in DEEPMD, NEQUIP/ALLEGRO codes respectively. For the training and validation sets, we employed the trajectory results from the ab-initio molecular dynamics (AIMD) and ground state DFT calculations including the energy, force, and virial database from highly diverse compositions, temperatures, and pressures following a "High Entropy Strategy". We systematically developed the Deep Learning potentials for 5, 7, and 10 component systems based on the complexity level of the phase mixtures. To optimize the hyperparameters, we used a series of machine learning (ML) algorithms to lower the RMSE of the force components. We then compare the accuracy of both the potentials developed using the two types of Deep Learning potentials through a variety of large-scale molecular dynamics (MD) simulations.