First-principles calculations are used to characterize the bulk elastic properties of cubic and tetragonal phase metal dihydrides, MH2 {M = Sc, Y, Ti, Zr, Hf, lanthanides} to gain insight into the mechanical properties that govern the aging behavior of rare-earth di-tritides as the constituent 3H, tritium, decays into 3He. As tritium decays, helium is inserted in the lattice, the helium migrates and collects into bubbles, that then can ultimately create sufficient internal pressure to rupture the material. The elastic properties of the materials are needed to construct effective mesoscale models of the process of bubble growth and fracture. Dihydrides of the scandium column and most of the rareearths crystalize into a cubic phase, while dihydrides from the next column, Ti, Zr, and Hf, distort instead into the tetragonal phase, indicating incipient instabilities in the phase and potentially significant changes in elastic properties. We report the computed elastic properties of these dihydrides, and also investigate the off-stoichiometric phases as He or vacancies accumulate. As helium builds up in the cubic phase, the shear moduli greatly soften, converting to the tetragonal phase. Conversely, the tetragonal phases convert very quickly to cubic with the removal of H from the lattice, while the cubic phases show little change with removal of H. The source and magnitude of the numerical and physical uncertainties in the modeling are analyzed and quantified to establish the level of confidence that can be placed in the computational results, and this quantified confidence is used to justify using the results to augment and even supplant experimental measurements.
Carbon nanostructures, such as nanotubes and graphene, are of considerable interest due to their unique mechanical and electrical properties. The materials exhibit extremely high strength and conductivity when defects created during synthesis are minimized. Atomistic modeling is one technique for high resolution studies of defect formation and mitigation. To enable simulations of the mechanical behavior and growth mechanisms of C nanostructures, a high-fidelity analytical bond-order potential for the C is needed. To generate inputs for developing such a potential, we performed quantum mechanical calculations of various C structures.
In this project we developed t he atomistic models needed to predict how graphene grows when carbon is deposited on metal and semiconductor surfaces. We first calculated energies of many carbon configurations using first principles electronic structure calculations and then used these energies to construct an empirical bond order potentials that enable s comprehensive molecular dynamics simulation of growth. We validated our approach by comparing our predictions to experiments of graphene growth on Ir, Cu and Ge. The robustness of ou r understanding of graphene growth will enable high quality graphene to be grown on novel substrates which will expand the number of potential types of graphene electronic devices.
This Report characterizes the defects in the def ect reaction network in silicon - doped, n - type InAs predicted with first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in Si - doped InAs , until culminating in immobile reaction p roducts. The defect reactions and reaction energies are tabulated, along with the properties of all the silicon - related defects in the reaction network. This Report serves to extend the results for the properties of intrinsic defects in bulk InAs as colla ted in SAND 2013 - 2477 : Simple intrinsic defects in InAs : Numerical predictions to include Si - containing simple defects likely to be present in a radiation - induced defect reaction sequence . This page intentionally left blank
This report summarizes the result of LDRD project 12-0395, titled "Automated Algorithms for Quantum-level Accuracy in Atomistic Simulations." During the course of this LDRD, we have developed an interatomic potential for solids and liquids called Spectral Neighbor Analysis Poten- tial (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The SNAP coef- ficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. Global optimization methods in the DAKOTA software package are used to seek out good choices of hyperparameters that define the overall structure of the SNAP potential. FitSnap.py, a Python-based software pack- age interfacing to both LAMMPS and DAKOTA is used to formulate the linear regression problem, solve it, and analyze the accuracy of the resultant SNAP potential. We describe a SNAP potential for tantalum that accurately reproduces a variety of solid and liquid properties. Most significantly, in contrast to existing tantalum potentials, SNAP correctly predicts the Peierls barrier for screw dislocation motion. We also present results from SNAP potentials generated for indium phosphide (InP) and silica (SiO 2 ). We describe efficient algorithms for calculating SNAP forces and energies in molecular dynamics simulations using massively parallel computers and advanced processor ar- chitectures. Finally, we briefly describe the MSM method for efficient calculation of electrostatic interactions on massively parallel computers.
This report documents the development, demonstration and validation of a mesoscale, microstructural evolution model for simulation of zirconium hydride {delta}-ZrH{sub 1.5} precipitation in the cladding of used nuclear fuels that may occur during long-term dry storage. While the Zr-based claddings are manufactured free of any hydrogen, they absorb hydrogen during service, in the reactor by a process commonly termed ‘hydrogen pick-up’. The precipitation and growth of zirconium hydrides during dry storage is one of the most likely fuel rod integrity failure mechanisms either by embrittlement or delayed hydride cracking of the cladding. While the phenomenon is well documented and identified as a potential key failure mechanism during long-term dry storage (NUREG/CR-7116), the ability to actually predict the formation of hydrides is poor. The model being documented in this work is a computational capability for the prediction of hydride formation in different claddings of used nuclear fuels. This work supports the Used Fuel Disposition Research and Development Campaign in assessing the structural engineering performance of the cladding during and after long-term dry storage. This document demonstrates a basic hydride precipitation model that is built on a recently developed hybrid Potts-phase field model that combines elements of Potts-Monte Carlo and the phase-field models. The model capabilities are demonstrated along with the incorporation of the starting microstructure, thermodynamics of the Zr-H system and the hydride formation mechanism.
This Report characterizes the defects in the defect reaction network in silicon-doped, n-type InP deduced from first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in Si-doped InP until culminating in immobile reaction products. The defect reactions and reaction energies are tabulated, along with the properties of all the silicon-related defects in the reaction network. This Report serves to extend the results for intrinsic defects in SAND 2012-3313: %E2%80%9CSimple intrinsic defects in InP: Numerical predictions%E2%80%9D to include Si-containing simple defects likely to be present in a radiation-induced defect reaction sequence.
This Report presents numerical tables summarizing properties of intrinsic defects in indium arsenide, InAs, as computed by density functional theory using semi-local density functionals, intended for use as reference tables for a defect physics package in device models.