Date of Graduation

Spring 2023


Master of Science in Materials Science


Physics, Astronomy, and Materials Science

Committee Chair

Ridwan Sakidja


The interatomic potentials designed for binary/high entropy diborides and ultra-high temperature composites (UHTC) have been developed through the implementation of deep neural network (DNN) algorithms. These algorithms employed two different approaches and corresponding codes; 1) strictly local & invariant scalar-based descriptors as implemented in the DEEPMD code and 2) equivariant tensor-based descriptors as included in the ALLEGRO code. The samples for training and validation sets of the forces, energy, and virial data were obtained from the ab-initio molecular dynamics (AIMD) simulations and Density Functional Theory (DFT) calculations, including the simulation data from the ultra-high temperature region (> 2000K). The study then compared the accuracy of the Deep Learning potentials to predict not only the ground-state properties, such as the elastic constants and the phonon dispersion curves but also the ultra-high temperature properties, including the lattice parameters and melting behaviors.


interatomic potential, molecular dynamics, thermal properties, high entropy diborides, ultra-high temperature ceramics, artificial intelligence

Subject Categories

Atomic, Molecular and Optical Physics | Ceramic Materials | Engineering Physics | Other Materials Science and Engineering


© Nur Aziz Octoviawan

Open Access