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Full-Text Articles in Physics
Machine Learning Strategies For Potential Development In High-Entropy Driven Nickel-Based Superalloys, Marium Mostafiz Mou
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 …
Development Of Interatomic Potential Of High Entropy Diborides With Artificial Intelligence Approach To Simulate The Thermo-Mechanical Properties, Nur Aziz Octoviawan
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 …
Review Of Current Reactive Force Field Potentials For Use In Simulating The Atomic Layer Deposition Of Alumina On Aluminum, Devon T. Romine
Review Of Current Reactive Force Field Potentials For Use In Simulating The Atomic Layer Deposition Of Alumina On Aluminum, Devon T. Romine
MSU Graduate Theses
Alumina has recently garnered quite a bit of attention for use as a tunnel barrier in Josephson tunnel junctions. The quality of the metal oxide layer in the Josephson tunnel junction is a key factor in its effectiveness. To optimize the deposition method of alumina, we need a deep understanding of the large-scale surface interactions that cannot be reached using ab initio molecular dynamics. In this study, I have compared two existing reactive force field (ReaxFF) parameters to determine their abilities to model the atomic layer deposition (ALD) of alumina on an aluminum surface. ReaxFF molecular dynamics was chosen because …
Study Of Amorphous Boron Carbide And Hydrogenated Boron Carbide Materials Using Molecular Dynamics And Hybrid Reverse Monte Carlo, Rajan Khadka
MSU Graduate Theses
We present a computational study of amorphous boron carbide (a-BxC) models using Molecular Dynamics (MD) studied with Stillinger-Weber (SW) and ReaxFF potential. The atomic structure factor (S(Q)), radial distribution function (RDF) and bond lengths comparison with other experimental and ab initio models shows that a random arrangement of icosahedra (B12, B11C) interconnected by chains (CCC, CBC) are present in a-BxC. Afterward, Hybrid Reverse Monte Carlo (HRMC) technique is used to recreate a-BxC structures. The existing SW potential parameters of Boron are optimized for the α-rhombohedral (Icosahedral B12 …