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Thermal Conductivity And Mechanical Properties Of Interlayer-Bonded Graphene Bilayers, Afnan Mostafa

Masters Theses

Graphene, an allotrope of carbon, has demonstrated exceptional mechanical, thermal, electronic, and optical properties. Complementary to such innate properties, structural modification through chemical functionalization or defect engineering can significantly enhance the properties and functionality of graphene and its derivatives. Hence, understanding structure-property relationships in graphene-based metamaterials has garnered much attention in recent years. In this thesis, we present molecular dynamics studies aimed at elucidating structure-property relationships that govern the thermomechanical response of interlayer-bonded graphene bilayers.

First, we present a systematic and thorough analysis of thermal transport in interlayer-bonded twisted bilayer graphene (IB-TBG). We find that the introduction of interlayer C-C …

Development Of Interatomic Potential Of High Entropy Diborides With Artificial Intelligence Approach To Simulate The Thermo-Mechanical Properties, Nur Aziz Octoviawan

MSU Graduate Theses

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 …

Machine Learning Strategies For Potential Development In High-Entropy Driven Nickel-Based Superalloys, Marium Mostafiz Mou

MSU Graduate Theses

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 …