Date of Graduation

Spring 2023


Master of Science in Materials Science


Physics, Astronomy, and Materials Science

Committee Chair

Ridwan Sakidja


In this study, I developed Deep Learning interatomic potentials to model a multi-phase and multi-component system of Ni-based Superalloys. The system has up to three major phase constituents, namely Gamma, Gamma Prime, and Transition-metal rich Carbide. I utilized invariant scalar-based and/or equivariant, tensor-based neural network (NN) approach as implemented in DEEPMD, NEQUIP/ALLEGRO codes, respectively, and Moment Tensor Potential (MTP). For the training and validation sets, I employed the ab-initio molecular dynamics (AIMD) trajectory results and ground state DFT calculations, including the energy, force, and virial database from highly diverse compositions, temperatures, and pressures following a “High Entropy Strategy.” The Deep learning potential was systematically developed for 4, 5, 7, and 10 component systems based on the complexity level of the phase mixtures. To optimize the hyperparameters, I used a series of machine learning (ML) algorithms to lower the RMSE of the force components and 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 The GPU-based supercomputer support from NERSC (Perlmutter) is gratefully acknowledged.


superalloy, nickel, machine learning, neural network, molecular dynamics, high entropy

Subject Categories

Artificial Intelligence and Robotics | Atomic, Molecular and Optical Physics | Metallurgy


© Marium Mostafiz Mou

Open Access