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The development of radiation-tolerant structural materials is an essential element for the success of advanced nuclear energy concepts. A proven strategy to increase radiation resistance is to create microstructures with a high density of internal defect sinks, such as grain boundaries (GBs). However, as GBs absorb defects, they undergo internal transformations that limit their ability to capture defects indefinitely. Here, we show that, as the sink efficiency of GBs becomes exhausted with increasing irradiation dose, networks of irradiation loops form in the vicinity of saturated or near-saturated GB, maintaining and even increasing their capacity to continue absorbing defects. The formation of these networks fundamentally changes the driving force for defect absorption at GB, from chemical to elastic. Using thermally-activated dislocation dynamics simulations, we show that these networks are consistent with experimental measurements of defect densities near GB. Our results point to these networks as a natural continuation of the GB once they exhaust their internal defect absorption capacity.

Laser-powder bed fusion additive manufacturing (LPBF-AM) of metals is rapidly becoming one of the most important materials processing pathways for next-generation metallic parts and components in a number of important applications. However, the large parametric space that characterizes laser-based LPBF-AM makes it challenging to understand what are the variables controlling the microstructural and mechanical property outcomes. Sensitivity studies based on direct LPBF-AM processing are costly and lengthy to conduct, and are subjected to the specifications and variability of each printer. Here we develop a fast-throughput numerical approach that simulates the LPBF-AM process using a cellular automaton model of dynamic solidification and grain growth. This is accompanied by a polycrystal plasticity model that captures grain boundary strengthening due to complex grain geometry and furnishes the stress-strain curves of the resulting microstructures. Our approach connects the processing stage with the mechanical testing stage, thus capturing the effect of processing variables such as the laser power, laser spot size, scan speed, and hatch width on the yield strength and tangent moduli of the processed materials. When applied to pure Cu and stainless 316L steel, we find that laser power and scan speed have the strongest influence on grain size in each material, respectively.

Structural materials properties are highly dependent on their microstructure. Their microstructure is in turn affected by multiple fabrication and thermo-mechanical treatment parameters, all of which conform a highly-dimensional parametric space with often hidden correlations that are difficult to extract by experimentation alone. This is particularly true for alloys of the dual-phase Ti-6Al-4V family, with their greatly complex and rich microstructures, which combine several intrinsic lengthscales associated with multiple grain and subgrain structures, grains with different crystal lattices (α and β phases), and complex chemistry. Here we use a comprehensive set of machine learning techniques to develop predictive tools relating the yield strength and hardening rate of these alloys to a set of input parameters covering extensive ranges. The data generator is a finite-element crystal plasticity model for polycrystal deformation that takes into account slip anisotropy and employs standard dislocation evolution models for the α and β phases of Ti-based alloys. Our dataset includes over two thousand independent simulations and is used to train the machine learning models, which are then used to establish correlations between microstructural parameters and the alloys’ mechanical response. Our results point to the most influential parameters affecting yield strength and hardening rate, information that can then be used to guide experimental synthesis and characterization efforts to save time and resources.

In this work, we study vacancy energetics in the equiatomic Nb-Mo-Ta-W alloy, especially vacancy formation and migration energies, using molecular statics calculations based on a spectral neighbor analysis potential specifically developed for Nb-Mo-Ta-W. We consider vacancy properties in bulk environments as well as near edge dislocation cores, including the effect of short-range order (SRO) by preparing supercells through Metropolis Monte-Carlo relaxations and temperature on the calculation. The nudged elastic band (NEB) method is applied to study vacancy migration energies. Our results show that both vacancy formation energies and vacancy migration energies are statistically distributed with a wide spread, on the order of 1.0 eV in some cases, and display a noticeable dependence on SRO. We find that, in some cases, vacancies can form with very low energies at edge dislocation cores, from which we hypothesize the formation of stable 'superjogs' on edge dislocation lines. Moreover, the large spread in vacancy formation energies results in an asymmetric thermal sampling of the formation energy distribution towards lower values. This gives rise to effective vacancy formation energies that are noticeably lower than the distribution averages. We study the effect that this phenomenon has on the vacancy diffusivity in the alloy and discuss the implications of our findings on the structural features of Nb-Mo-Ta-W.

The Portevin-Le Chatelier (PLC) effect is a phenomenon by which plastic slip in metallic materials becomes unstable, resulting in jerky flow and the onset of inhomogeneous deformation. The PLC effect is thought to be fundamentally caused by the dynamic interplay between dislocations and solute atoms. However, this interplay is almost always inaccessible experimentally due to the extremely fine length and time scales over which it occurs. In this paper, simulations of jerky flow in W-O interstitial solid solutions reveal three dynamic regimes emerging from the simulated strain rate-temperature space: one resembling standard solid solution strengthening, another one mimicking solute cloud formation, and a third one where dislocation/solute coevolution leads to jerky flow as a precursor of dynamic strain aging. The simulations are carried out in a stochastic framework that naturally captures rare events in a rigorous manner, providing atomistic resolution over diffusive time scales using no adjustable parameters.

The formation of elongated zirconium hydride platelets during corrosion of nuclear fuel clad is linked to its premature failure due to embrittlement and delayed hydride cracking. Despite their importance, however, most existing models of hydride nucleation and growth in Zr alloys are phenomenological and lack sufficient physical detail to become predictive under the variety of conditions found in nuclear reactors during operation. Moreover, most models ignore the dynamic nature of clad oxidation, which requires that hydrogen transport and precipitation be considered in a scenario where the oxide layer is continuously growing at the expense of the metal substrate. In this paper, we perform simulations of hydride formation in Zr clads with a moving oxide/metal boundary using a stochastic kinetic diffusion/reaction model parameterized with state-of-the-art defect and solute energetics. Our model uses the solutions of the hydrogen diffusion problem across an increasingly-coarse oxide layer to define boundary conditions for the kinetic simulations of hydrogen penetration, precipitation, and dissolution in the metal clad. Our method captures the spatial dependence of the problem by discretizing all spatial derivatives using a stochastic finite difference scheme. Our results include hydride number densities and size distributions along the radial coordinate of the clad for the first 1.6 h of evolution, providing a quantitative picture of hydride incipient nucleation and growth under clad service conditions.

The Portevin-Le Chatelier (PLC) effect is a phenomenon by which plastic slip in metallic materials becomes unstable, resulting in jerky flow and the onset of inhomogeneous deformation. The PLC effect is thought to be fundamentally caused by the dynamic interplay between dislocations and solute atoms. However, this interplay is almost always inaccessible experimentally due to the extremely fine length and time scales over which it occurs. In this paper, simulations of jerky flow in W-O interstitial solid solutions reveal three dynamic regimes emerging from the simulated strain rate-temperature space: one resembling standard solid solution strengthening, another one mimicking solute cloud formation, and a third one where dislocation/solute coevolution leads to jerky flow as a precursor of dynamic strain aging. The simulations are carried out in a stochastic framework that naturally captures rare events in a rigorous manner, providing atomistic resolution over diffusive time scales using no adjustable parameters.

Surface erosion and secondary electron emission (SEE) have been identified asthe most critical life-limiting factors in channel walls of Hall-effectthrusters for space propulsion. Recent wall concepts based on micro-architectedsurfaces have been proposed to mitigate surface erosion and SEE. The ideabehind these designs is to take advantage of very-high surface-to-volume ratiosto reduce SEE and ion erosion by internal trapping and redeposition. This hasresulted in renewed interest to study electron-electron processes in relevantthruster wall materials. In this work, we present calculations of SEE yields inmicro-porous hexagonal BN surfaces using stochastic simulations ofelectron-material interactions in discretized surface geometries. Our modelconsists of two complementary parts. First we study SEE as a function ofprimary electron energy and incidence angle in flat surfaces using Monte Carlosimulations of electron multi-scattering processes. The results are then usedto represent the response function of discrete surface elements to individualelectron rays generated using a ray-tracing Monte Carlo model. We find thatmicro-porous surfaces result in SEE yield reductions of over 50% in the energyrange experienced in Hall thrusters. This points to the suitability of thesemicro-architected surface concepts to mitigate SEE-related issues in compactelectric propulsion devices.

Surface erosion and secondary electron emission (SEE) have been identified asthe most critical life-limiting factors in channel walls of Hall-effectthrusters for space propulsion. Recent wall concepts based on micro-architectedsurfaces have been proposed to mitigate surface erosion and SEE. The ideabehind these designs is to take advantage of very-high surface-to-volume ratiosto reduce SEE and ion erosion by internal trapping and redeposition. This hasresulted in renewed interest to study electron-electron processes in relevantthruster wall materials. In this work, we present calculations of SEE yields inmicro-porous hexagonal BN surfaces using stochastic simulations ofelectron-material interactions in discretized surface geometries. Our modelconsists of two complementary parts. First we study SEE as a function ofprimary electron energy and incidence angle in flat surfaces using Monte Carlosimulations of electron multi-scattering processes. The results are then usedto represent the response function of discrete surface elements to individualelectron rays generated using a ray-tracing Monte Carlo model. We find thatmicro-porous surfaces result in SEE yield reductions of over 50% in the energyrange experienced in Hall thrusters. This points to the suitability of thesemicro-architected surface concepts to mitigate SEE-related issues in compactelectric propulsion devices.

Atomic configurations of glassy or amorphous materials containing medium-range order (MRO) may be identified by comparing fluctuation transmission electron microscopy (FTEM) measurements to FTEM simulations obtained using model configurations. Candidate model sizes have traditionally been much thinner than the samples measured experimentally, and publicly available FTEM simulation software has until now omitted microscope parameters, dynamical scattering, and the phase of the diffracted electron wave. We introduce MS-STEM-FEM, an open-source software package for simulating FTEM experiments using established multi-slice TEM simulation techniques to emulate experiment more closely by incorporating microscope parameters and simulating electron scattering and propagation as a complex valued wave. Simulations using established models are compared with results of experimental STEM-FEM to validate the software. Several statistical measures of diffraction are implemented and their responses to model features are compared. Dynamical scattering is found to be less influential than the variety of crystallite orientations which occur in thicker models. Simulations of variable resolution microscopy confirm that cumulative intensity of the FTEM signal decreases with reduced model MRO and increased coherence volume. Advantageous model scaling characteristics and efficient processor performance scaling are demonstrated, along with a study of convergence with respect to pertinent simulation parameters to identify accuracy requirements.

Atomic configurations of glassy or amorphous materials containing medium-range order (MRO) may be identified by comparing fluctuation transmission electron microscopy (FTEM) measurements to FTEM simulations obtained using model configurations. Candidate model sizes have traditionally been much thinner than the samples measured experimentally, and publicly available FTEM simulation software has until now omitted microscope parameters, dynamical scattering, and the phase of the diffracted electron wave. We introduce MS-STEM-FEM, an open-source software package for simulating FTEM experiments using established multi-slice TEM simulation techniques to emulate experiment more closely by incorporating microscope parameters and simulating electron scattering and propagation as a complex valued wave. Simulations using established models are compared with results of experimental STEM-FEM to validate the software. Several statistical measures of diffraction are implemented and their responses to model features are compared. Dynamical scattering is found to be less influential than the variety of crystallite orientations which occur in thicker models. Simulations of variable resolution microscopy confirm that cumulative intensity of the FTEM signal decreases with reduced model MRO and increased coherence volume. Advantageous model scaling characteristics and efficient processor performance scaling are demonstrated, along with a study of convergence with respect to pertinent simulation parameters to identify accuracy requirements.

We derive an Ising Hamiltonian for kinetic simulations involving interstitial and vacancy defects in binary alloys. Our model, which we term 'ABVI', incorporates solute transport by both interstitial defects and vacancies into a mathematically-consistent framework, and thus represents a generalization to the widely-used ABV model for alloy evolution simulations. The Hamiltonian captures the three possible interstitial configurations in a binary alloy: A-A, A-B, and B-B, which makes it particularly useful for irradiation damage simulations. All the constants of the Hamiltonian are expressed in terms of bond energies that can be computed using first-principles calculations. We implement our ABVI model in kinetic Monte Carlo simulations and perform a verification exercise by comparing our results to published irradiation damage simulations in simple binary systems with Frenkel pair defect production and several microstructural scenarios, with matching agreement found.

We derive an Ising Hamiltonian for kinetic simulations involving interstitial and vacancy defects in binary alloys. Our model, which we term 'ABVI', incorporates solute transport by both interstitial defects and vacancies into a mathematically-consistent framework, and thus represents a generalization to the widely-used ABV model for alloy evolution simulations. The Hamiltonian captures the three possible interstitial configurations in a binary alloy: A-A, A-B, and B-B, which makes it particularly useful for irradiation damage simulations. All the constants of the Hamiltonian are expressed in terms of bond energies that can be computed using first-principles calculations. We implement our ABVI model in kinetic Monte Carlo simulations and perform a verification exercise by comparing our results to published irradiation damage simulations in simple binary systems with Frenkel pair defect production and several microstructural scenarios, with matching agreement found.

Due to their high strength and advantageous high-temperature properties, tungsten-based alloys are being considered as plasma-facing candidate materials in fusion devices. Under neutron irradiation, rhenium, which is produced by nuclear transmutation, has been found to precipitate in elongated precipitates forming thermodynamic intermetallic phases at concentrations well below the solubility limit. Recent measurements have shown that Re precipitation can lead to substantial hardening, which may have a detrimental effect on the fracture toughness of W alloys. This puzzle of sub-solubility precipitation points to the role played by irradiation induced defects, specifically mixed solute-W interstitials. Here, using first-principles calculations based on density functional theory, we study the energetics of mixed interstitial defects in W-Re, W-V, and W-Ti alloys, as well as the heat of mixing for each substitutional solute. We find that mixed interstitials in all systems are strongly attracted to each other with binding energies of −2.4 to −3.2 eV and form interstitial pairs that are aligned along parallel first-neighbor 〈111〉 strings. Low barriers for defect translation and rotation enable defect agglomeration and alignment even at moderate temperatures. We propose that these elongated agglomerates of mixed-interstitials may act as precursors for the formation of needle-shaped intermetallic precipitates. This interstitial-based mechanism is not limited to radiation induced segregation and precipitation in W–Re alloys but is also applicable to other body-centered cubic alloys.

© 2015 Elsevier Ltd. We use a physically-based crystal plasticity model to predict the yield strength of body-centered cubic (bcc) tungsten single crystals subjected to uniaxial loading. Our model captures the thermally-activated character of screw dislocation motion and full non-Schmid effects, both of which are known to play critical roles in bcc plasticity. The model uses atomistic calculations as the sole source of constitutive information, with no parameter fitting of any kind to experimental data. Our results are in excellent agreement with experimental measurements of the yield stress as a function of temperature for a number of loading orientations. The validated methodology is employed to calculate the temperature and strain-rate dependence of the yield strength for 231 crystallographic orientations within the standard stereographic triangle. We extract the strain-rate sensitivity of W crystals at different temperatures, and finish with the calculation of yield surfaces under biaxial loading conditions that can be used to define effective yield criteria for engineering design models.

Germanium is an extremely important material used for numerous functional applications in many fields of nanotechnology. In this paper, we study the crystallization of amorphous Ge using atomistic simulations of critical nano-metric nuclei at high temperatures. We find that crystallization occurs by the recurrent transfer of atoms via a diffusive process from the amorphous phase into suitably-oriented crystalline layers. We accompany our simulations with a comprehensive thermodynamic and kinetic analysis of the growth process, which explains the energy balance and the interfacial growth velocities governing grain growth. For the 〈111〉 crystallographic orientation, we find a degenerate atomic rearrangement process, with two zero-energy modes corresponding to a perfect crystalline structure and the formation of a Σ3 twin boundary. Continued growth in this direction results in the development a twin network, in contrast with all other growth orientations, where the crystal grows defect-free. This particular mechanism of crystallization from amorphous phases is also observed during solid-phase epitaxial growth of 〈111〉 semiconductor crystals, where growth is restrained to one dimension. We calculate the equivalent X-ray diffraction pattern of the obtained nanotwin networks, providing grounds for experimental validation.

An improved version of a recently developed stochastic cluster dynamics (SCD) method (Marian and Bulatov, 2012) [6] is introduced as an alternative to rate theory (RT) methods for solving coupled ordinary differential equation (ODE) systems for irradiation damage simulations. SCD circumvents by design the curse of dimensionality of the variable space that renders traditional ODE-based RT approaches inefficient when handling complex defect population comprised of multiple (more than two) defect species. Several improvements introduced here enable efficient and accurate simulations of irradiated materials up to realistic (high) damage doses characteristic of next-generation nuclear systems. The first improvement is a procedure for efficiently updating the defect reaction-network and event selection in the context of a dynamically expanding reaction-network. Next is a novel implementation of the τ-leaping method that speeds up SCD simulations by advancing the state of the reaction network in large time increments when appropriate. Lastly, a volume rescaling procedure is introduced to control the computational complexity of the expanding reaction-network through occasional reductions of the defect population while maintaining accurate statistics. The enhanced SCD method is then applied to model defect cluster accumulation in iron thin films subjected to triple ion-beam (Fe3+, He+ and H+) irradiations, for which standard RT or spatially-resolved kinetic Monte Carlo simulations are prohibitively expensive.

© 2014 Elsevier B.V. Under fusion reactor conditions, large quantities of irradiation defects and transmutation gases are produced per unit time by neutrons, resulting in accelerated degradation of structural candidate ferritic (F) and ferritic/martensitic (F/M) steels. Due to the lack of a suitable fusion neutron testing facility, we must rely on high-dose-rate ion-beam experiments and present-day crude modeling estimates. Of particular interest is the possibility of synergistic (positive feedback) effects on materials properties due to the simultaneous action of He, H, and displacement damage (dpa) during operation. In this paper we discuss the state-of-the-art in terms of the experimental understanding of synergistic effects and carry out simulations of triple-species irradiation under ion-beam conditions using first-of-its-kind modeling techniques. Although, state-of-the-art modeling and simulation is not sufficiently well developed to shed light on the experimental uncertainties, we are able to conclude that it is not clear whether synergistic effects, the evidence of which is still not conclusive, will ultimately play a critical role in material performance under fusion energy conditions. We review here some of the evidence for the synergistic effects of hydrogen in the presence of helium and displacement damage, and also include some recent data from our research. While the experimental results to date suggest possible mechanisms for the observed synergistic effects, it is only with more advanced modeling that we can hope to understand the details underlying the experimental observations. By employing modeling and simulation we propose an interaction model that is qualitatively consistent with experimental observations of dpa/He/H irradiation behavior. Our modeling, the results of which should be helpful to researchers going forward, points to gaps and voids in the current understanding of triple ion-beam irradiation effects (displacement damage produced simultaneously with helium and hydrogen implantation) and the synergistic effects of hydrogen.

Thermally-activated 1/2〈111〉 screw dislocation motion is the controlling plastic mechanism at low temperatures in body-centered cubic (bcc) crystals. Dislocation motion proceeds by nucleation and propagation of atomic-sized kink pairs in close-packed planes. The atomistic character of kink pairs can be studied using techniques such as molecular dynamics (MD). However, MD's natural inability to properly sample thermally-activated processes as well as to capture {110} screw dislocation glide calls for the development of other methods capable of overcoming these limitations. Here we develop a kinetic Monte Carlo (kMC) approach to study single screw dislocation dynamics from room temperature to 0.5Tm and at stresses 0

Germanium is an extremely important material used for numerous functional applications in many fields of nanotechnology. In this paper, we study the crystallization of amorphous Ge using atomistic simulations of critical nano-metric nuclei at high temperatures. We find that crystallization occurs by the recurrent transfer of atoms via a diffusive process from the amorphous phase into suitably-oriented crystalline layers. We accompany our simulations with a comprehensive thermodynamic and kinetic analysis of the growth process, which explains the energy balance and the interfacial growth velocities governing grain growth. For the 〈111〉 crystallographic orientation, we find a degenerate atomic rearrangement process, with two zero-energy modes corresponding to a perfect crystalline structure and the formation of a Σ3 twin boundary. Continued growth in this direction results in the development a twin network, in contrast with all other growth orientations, where the crystal grows defect-free. This particular mechanism of crystallization from amorphous phases is also observed during solid-phase epitaxial growth of 〈111〉 semiconductor crystals, where growth is restrained to one dimension. We calculate the equivalent X-ray diffraction pattern of the obtained nanotwin networks, providing grounds for experimental validation.

The kinetic Monte Carlo (kMC) method is used in many scientific fields in applications involving rare-event transitions. Due to its discrete stochastic nature, efforts to parallelize kMC approaches often produce unbalanced time evolutions requiring complex implementations to ensure correct statistics. In the context of parallel kMC, the sequential update technique has shown promise by generating high quality distributions with high relative efficiencies for short-range systems. In this work, we provide an extension of the sequential update method in a parallel context that rigorously obeys detailed balance, which guarantees exact equilibrium statistics for all parallelization settings. Our approach also preserves nonequilibrium dynamics with minimal error for many parallelization settings, and can be used to achieve highly precise sampling. © 2014 Elsevier B.V. All rights reserved.