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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 …
Dynamical Properties Of Ferroelectric Perovskites (Ba,Sr)Tio3 And Pb(Zr,Ti)O3 Systems From First Principles, Jeevaka Weerasinghe
Dynamical Properties Of Ferroelectric Perovskites (Ba,Sr)Tio3 And Pb(Zr,Ti)O3 Systems From First Principles, Jeevaka Weerasinghe
Graduate Theses and Dissertations
A first-principles-based effective Hamiltonian scheme which incorporates coupling between ferroelectric (FE) and antiferrodistortive (AFD) motions is applied to Pb(Zr,Ti)O3 alloys. It validates the existence of two modes of E symmetry (rather than the single E(1TO) soft mode) in the 50-75 cm-1 range for temperatures smaller than 200 K and for compositions falling within the Rhombohedral R3c phase. Coupling between long-range-ordered FE and AFD motions is shown to be the cause of the additional mode and more insight into its nature is provided. This scheme is further used to reveal a field-induced anticrossing involving FE and AFD degrees of …