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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Atomic-scale understanding of oxide growth and dissolution kinetics of Ni-Cr alloys

Aqueous corrosion of metals is governed by formation and dissolution of a passivating, multi-component surface oxide. Unfortunately, a detailed atomistic description is challenging due to the compositional complexity and the need to consider multiple kinetic factors simultaneously. To this end, we combine experiments with a first-principles-derived, multiscale computational framework that transcends thermodynamic descriptions to explicitly simulate the kinetic evolution of surface oxides of Ni-Cr alloys as a function of composition, temperature, pH, and applied voltage. In the absence of pitting, we identify three distinct voltage regimes, which are kinetically dominated by oxide growth, dissolution, and competitive dissolution and reprecipitation. Evolving compositional gradients and oxide thickness are revealed, including a transition between a metastable Ni-Cr mixed oxide and a thick, porous Ni-dominated oxide. Beyond elucidating the underlying physics, we highlight the need for competing kinetics in models to properly predict the transition from passivation to corrosion. Our results provide a key step towards co-design of alloy composition alongside environmental conditions for sustainable use across a variety of critical energy and infrastructure applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine learning enabled quantification of the hydrogen bonds inside the polyelectrolyte brush layer probed using all-atom molecular dynamics simulations

The configuration of densely grafted charged polyelectrolyte (PE) brushes is strongly dictated by the properties and behavior of the counterions that screen the PE brush charges and the solvent molecules (typically water) that solvate the brush molecules and these screening counterions. Only recently, efforts have been made to study the PE brushes atomistically, thereby shedding light on the properties of brush-supported ions and water molecules. However, even for such efforts, there are limitations associated with using a generic definition to estimate certain properties of water and ions inside the brush layer. For example, water–water hydrogen bonds (HBs) will behave differently for locations outside and inside the brush layer, given the fact that the densely closely grafted PE brush molecules create a soft nanoconfinement where the water connectivity becomes highly disrupted: therefore, using the same definition to quantify the HBs inside and outside the brush layer will be unwise. In this paper, we address this limitation by employing an unsupervised machine learning (ML) approach to predict the water–water hydrogen bonding inside a cationic PE brush layer modeled using all-atom molecular dynamics (MD) simulations. Here, the ML method, which relies on a clustering approach and uses the equilibrium coordinates of the water molecules (obtained from the all-atom MD simulations) as the input, is capable of identifying the structural modification of water–water HBs (revealed through appropriate clustering of the data) inside the PE brush layer induced soft nanoconfinement. Such capabilities would not have been possible by using a generic definition of the HBs. Our calculations lead to four key findings: (1) the clusters formed inside and outside the brush layer are structurally similar; (2) the margin of the cluster is shorter inside the PE brush layer confirming the possible disruption of the HBs inside the PE brush layer; (3) the average “hydrogen–acceptor-oxygen–donor-oxygen” angle that defines the HB is reduced for the HBs formed inside the brush layer; (4) the use of the generic definition (definition usable for characterizing the HBs in brush-free bulk) leads to an overprediction of the number of HBs formed inside the PE brush layer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Role of nanoscale coherent precipitates on the thermo-mechanical response of martensitic materials (Final Report)

Coherent second phases can have a profound effect on the properties and performance of martensitic materials, and we lack a comprehensive, mechanistic understanding of the underlying processes. This limits our ability to rationally design second phases to tailor the response of martensitic materials. To address this situation, this project sought to develop a mechanistic, predictive understanding of the thermal and mechanical response of martensitic materials with nanoscale coherent heterogeneities. A synergistic combination of atomistic simulations and experiments was used to relate the local properties of each phase (in particular, their free energy landscape) and the material nanostructure (volume fraction, shape and size of second phase precipitates, and defects that may disrupt coherency) to the overall materials response. The focus was on second phases expected to induce phenomena or properties not otherwise achievable: second order martensitic transformation in SMAs, ultra-low stiffness, increased control of transition temperatures, and fatigue resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantized Grain Boundary States Promote Nanoparticle Alignment During Imperfect Oriented Attachment

Oriented attachment (OA) has become a well-recognized mechanism for the growth of metal, ceramic, and biomineral crystals. While many computational and experimental studies of OA have shown that particles can attach with some misorientation then rotate to remove adjoining grain boundaries, the underlying atomistic pathways for this “Imperfect OA” process remain the subject of debate. In this paper, molecular dynamics and in situ TEM were used to probe the crystallographic evolution of up to 30 gold and copper nanoparticles during aggregation. It was found that Imperfect OA occurs because (1) grain boundaries become quantized when their size is comparable to the separation between constituent dislocations and (2) kinetic barriers associated with the glide of grain boundary dislocations are small. In support of these findings, TEM experiments show the formation of a single crystal aggregate after annealing 9 initially misoriented, agglomerated particles with evidence of dislocation slip and twin formation during particle/grain alignment. These observations motivate future work on assembled nanocrystals with tailored defects and call for a revision of Read-Shockley models for grain boundary energies in nanocrystalline materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

From Micro-environments to Macroscopic Effects: How the Alkaline Hydrogen Evolution Reaction Drives Cu Cathodic Corrosion

Cathodic corrosion of copper (Cu) has posed a significant challenge for over a century, impeding various technological progresses such as electrochemical conversion of CO 2 (eCO 2 RR) into fuels and other value-added carbon products. Here, in this study, employing a combined Density Functional Theory (DFT) and kinetic Monte Carlo (kMC) simulation approach, we delve into the atomistic level mechanism driving this phenomenon in Cu. Our hypothesis posits the pivotal role of alkaline hydrogen evolution reaction (HER) in facilitating cathodic corrosion in Cu. We rigorously develop a pH-dependent hydroxide (OH) adsorption mechanism and calculate the equilibrium OH coverage (𝜃 OH ) at varying pH levels, the thermodynamic stability of subsurface oxygen (O sub ), as well as the Cu-vacancy mediated diffusion of subsurface oxygens (O sub ). Through comprehensive analysis, we establish correlation among various microenvironments, including oxygen diffusion in subsurface layers, pH-dependent OH adsorption, and Cu dissolution into the electrolyte as (Cu-OH) complexes. Furthermore, our investigation explores the correlation between surface coordination environment of active sites and cathodic corrosion of Cu. Finally, by integrating DFT-derived thermodynamic data into a kMC model, we successfully predict the formation of experimentally observed corrosion pits on Cu-surfaces. This combined approach not only advances our fundamental understanding of Cu cathodic corrosion but also offers insights crucial for developing effective corrosion mitigation strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

New insights into creep characteristics of calcium silicate hydrates at molecular level

The fundamental mechanisms under concrete creep are far from being fully understood, especially at the molecular level. Hereby, a calcium-silicate-hydrate (C-S-H) molecular model is developed to explain, for the first time, the creep characteristics at various stress states, temperature levels and water contents, which are not accessible experimentally. Rather tensile and compressive loadings, C-S-H only creeps under shear loadings originating from the sliding of the calcium silicate layers over each other as the interlayer component (water and ions) acts as a lubricator. A heterogeneous creep characteristic is observed. Elevated temperature reduces the interlayer lubricator viscosity and weakens the interfacial adhesion between the layers and the interlayer lubricator, which accelerates C-S-H creep. The removal of interlayer water enhances the creep resistance, resulting from the reduced interlayer space and enhanced interfacial adhesion. The atomic-level mechanisms explain the inter-CSH-particle behaviours at the microscale, which bridges the gap between atomistic simulation and microcosmic phenomenon.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

ZeoNet: 3D convolutional neural networks for predicting adsorption in nanoporous zeolites

Zeolites are one of the most widely used materials in the chemical industry due to their nanometer-sized pores that can adsorb and react upon molecules selectively. With hundreds of known framework topologies and hundreds of thousands of computationally predicted structures, the ability to rapidly predict zeolite performance allows researchers to prioritize their efforts on the most promising structures for a given application. Although the accuracy of forcefield-based atomistic simulations has advanced significantly in the past two decades, these simulations can be computationally expensive, especially for long-chain, complex molecules. Here, we present ZeoNet, a representation learning framework using convolutional neural networks (ConvNets) and 3D volumetric representations for predicting adsorption in zeolites. ZeoNet was trained on the task of predicting Henry's constants for adsorption, k H , of n-octadecane in more than 330 000 known and predicted zeolite materials. Employing a 3D grid based on the distances to solvent-accessible surfaces, a volumetric representation that can be generated efficiently, the best-performing ZeoNet achieved a correlation coefficient r 2 = 0.977 and a mean-squared error MSE = 3.8 in ln k H , which corresponds to an error of 9.3 kJ mol -1 in adsorption free energy. In comparison, a model based on hand-designed geometric features has values of r 2 = 0.783 and MSE = 35.7. ZeoNet is also relatively efficient and can process ≈8 structures per second on an Nvidia RTX 2080TI GPU, orders of magnitude faster than forcefield-based simulations. A systematic analysis was conducted to investigate how the choice of ConvNet architectures, the linear dimension (L) and spatial resolution (Δd) of the distance grids, batch size, optimizer, and learning rate impact the model performance. We found that ConvNets based on the ResNet architecture offer the best tradeoff between expressiveness and efficiency. The performance for all models reaches a plateau at L = 30–45 Å and depends less sensitively on grid resolution, with a small benefit around Δd = 0.30–0.45 Å. Finally, saliency maps were visualized to identify which regions of the materials contributed the most to model predictions. It was found, interestingly, that the predictions are driven primarily by the accessible pore volume rather than the region occupied by the framework atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Localization of Zn 2+ ions affects the structural folding and mechanics of Nereis virens Nvjp-1

Several biological organisms utilize metal-coordination bonds to produce remarkable materials, such as the jaw of the marine worm Nereis virens, where metal-coordination bonds yield remarkable hardness without mineralization. Though the structure of a major component of the jaw, the Nvjp-1 protein, has recently been resolved, a detailed nanostructural understanding of the role of metal ions on the structural and mechanical properties of the protein is missing, especially with respect to the localization of metal ions. In this work, atomistic replica exchange molecular dynamics with explicit water and Zn 2+ ions and steered molecular dynamics simulations were used to explore how the initial localization of the Zn 2+ ions impacts the structural folding and mechanical properties of Nvjp-1. We found that the initial distribution of metal ions for Nvjp-1, and likely for other proteins with high amounts of metal-coordination, has important effects on the resulting structure, with larger metal ion quantity resulting in a more compact structure. These structural compactness trends, however, are independent from the mechanical tensile strength of the protein, which increases with greater hydrogen bond content and uniform distribution of metal ions. Our results indicate that different physical principles underlie the structure or mechanics of Nvjp-1, with broader implications in the development optimized hardened bioinspired materials and the modeling of proteins with significant metal ion content.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Porosity in nuclear graphite and its impact on nuclear reactor science and criticality safety applications

Porosity in nuclear-grade graphite significantly influences its low-energy neutron scattering, yet its effect on underlying phonon properties remains debated. Here, this work integrates inelastic and small-angle neutron scattering (INS/SANS) experiments, advanced atomistic simulations with a novel machine-learned potential (DeepMD), total cross-section measurements, and neutronics calculations (SCALE, MCNP, OpenMC) to investigate porosity’s impact on neutron thermalization. INS measurements on diverse graphite grades reveal no discernible porosity effect on phonon spectra, which align with crystalline graphite. Conversely, total cross-section data below ≈10 meV show increased scattering attributable to SANS. Our DeepMD simulations demonstrate that realistic micropores do not distort phonon spectra, challenging the assumptions in current ENDF/B-VIII.1 porosity thermal scattering laws (TSLs). These TSLs, based on random atom removal, produce unphysical phonon spectra and inflate inelastic cross-sections. Augmenting a crystalline TSL with an SANS component accurately captures experimental total cross-sections. Neutronics benchmarks (ICSBEP/IRPhE) show ENDF porosity TSLs unphysically increase neutron multiplication factor, keff. Crucially, incorporating SANS physics (NCrystal/OpenMC) indicates accurately modeled porosity negligibly affects keff, reactor physics, or criticality safety.

Critical benchmarks↗
DOE OSTI2020

Coordinated Multiscale Modeling and Synthesis of Novel Nanostructured Composite Membranes for Solar Fuels Generation (Final Report)

Generation of carbon-based liquid fuels from reduction of CO 2 using solar energy requires the development of improved membranes that provide good ionic conductivity and mechanical properties while minimizing the crossover of gases and reduction products. Using closely coordinated multiscale modeling, synthesis and characterization studies we propose to investigate and develop novel nanostructured composite membranes with applications to conversion of carbon dioxide to storable chemical fuels. The envisioned proton exchange membranes (PEMs) will be based on a support of self- assembling polymer-modified nanoparticles with controllable porosity and tortuosity. The use of a nanoparticle support will provide control of transport and mechanical properties of the membranes. Tuning the copolymer modification of nanoparticles (polymer architectures, brush grafting density) will allow to optimize the ion (proton) transport via the Grotthuss mechanism through narrow hydrated channels while minimizing crossover of gases and reaction products due to the high density of the grafted brush and use of a non-ionic conducting glassy polymers to fill spaces between polymer-grafted nanoparticles. In Phase I synthesis of the copolymers and their attachment to the nanoparticle support will be guided by multiscale simulations that include atomistic molecular dynamics and continuum-level transport simulations. The former will include reactive and non-reactive atomistic simulations that will provide key insight into nanoscale polymer morphology and ion/molecular transport mechanisms need to optimize the copolymer structure. The latter will provide insight into the role of nanoparticle self-assembly on the global transport of ionic and molecular species. The proton conduction and CO 2 /methanol permeability of the membranes will be experimentally characterized as well as simulated and will be correlated with morphology and polymer structure. Prototypes of optimal membranes will be fabricated and characterized at the end of the project. Successful development of improved nanostructured composite PEMs and demonstration of the simulation-guided materials-by-design paradigm will result in our ability to design and synthesize membranes for a wide variety of solar fuels generation and related electrochemical applications.

14 SOLAR ENERGY↗
DOE OSTI2021

New experimental approach to understanding the chemical reactivity of oxide surfaces

Metal oxides have been an attractive option for a range of applications, including hydrogen sensors, microelectronics, and catalysis, due to their reactivity and tunability. The properties of metal oxides can vary greatly on their precise surface structure; however, few surface science techniques can achieve atomistic-level determinations of surface structure, and fewer yet can do so for insulator surfaces. Low energy ion beam analysis offers a potential insulator-compatible solution to characterizing the surface structure of metal oxides. As a feasibility study, we apply low energy ion beam analysis to investigate the surface structure of a magnetite single crystal, Fe 3 O 4 (100). We obtain multi-angle maps using both forward-scattering low energy ion scattering (LEIS) and backscattering impact-collision ion scattering spectroscopy (ICISS). Both sets of experimental maps have intensity patterns that reflect the symmetries of the Fe 3 O 4 (100) surface structure. However, analytical interpretation of these intensity patterns to extract details of the surface structure is significantly more complex than previous LEIS and ICISS structural studies of one-component metal crystals, which had far more symmetries to exploit. To gain further insight into the surface structure, we model our experimental measurements with ion-trajectory tracing simulations using molecular dynamics. Our simulations provide a qualitative indication that our experimental measurements agree better with a subsurface cation vacancy model than a distorted bulk model.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Co-design for Particle Applications at Exascale

Co-design across the Exascale Computing Project (ECP) has been critical for both enabling science applications and bringing disparate communities together. Developing and porting applications to the various high-performance computing (HPC) architectures on pre-exascale and exascale computers has been quite challenging due to the diversity of hardware features and software stacks. The Co-design Center for Particle Applications (CoPA) has developed and enhanced the Cabana and PROGRESS/BML libraries to facilitate the creation of new particle applications, make existing particle applications exascale capable, and allow teams to explore new capabilities. Particle methods from atomistic, mesoscale, continuum, through cosmological scales have been built with Cabana, along with new possibilities for application coupling. Similarly, the PROGRESS/BML library has enabled quantum particle applications with linear algebra solvers to use advanced hardware. Across these CoPA-developed libraries, the co-design abstraction layer combines performance portability with math library support to facilitate separation of concerns and directly support science runs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Voltage-Dependent First-Principles Simulation of Insertion of Chloride Ions into Al/Al 2 O 3 Interfaces Using the Quantum Continuum Approximation

Experiments have shown that pitting corrosion can develop in aluminum surfaces at potentials > − 0.5 V relative to the standard hydrogen electrode (SHE). Until recently, the onset of pitting corrosion in aluminum has not been rigorously explored at an atomistic scale because of the difficulty of incorporating a voltage into density functional theory (DFT) calculations. We introduce the Quantum Continuum Approximation (QCA) which self-consistently couples explicit DFT calculations of the metal-insulator and insulator-solution interfaces to continuum Poisson-Boltzmann electrostatic distributions describing the bulk of the insulating region. By decreasing the number of atoms necessary to explicitly simulate with DFT by an order of magnitude, QCA makes the first-principles prediction of the voltage of realistic electrochemical interfaces feasible. After developing this technique, we apply QCA to predict the formation energy of chloride atoms inserting into oxygen vacancies in Al(111)/α-Al 2 O 3 (0001) interfaces as a function of applied voltage. We predict that chloride insertion is only favorable in systems with a grain boundary in the Al 2 O 3 for voltages > − 0.2 V (SHE). Here our results roughly agree with the experimentally demonstrated onset of corrosion, demonstrating QCA's utility in modeling realistic electrochemical systems at reasonable computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Tuning Two-Dimensional Phthalocyanine Dual Site Metal–Organic Framework Catalysts for the Oxygen Reduction Reaction

Metal-organic frameworks (MOFs) offer an interesting opportunity for catalysis, particularly for metal-nitrogen-carbon (M-N-C) motifs by providing an organized porous structural pattern and well-defined active sites for the oxygen reduction reaction (ORR), a key need for hydrogen fuel cells and related sustainable energy technologies. Here, in this work, we leverage electrochemical testing with computational models to study the electronic and structural properties in these systems and their relationship to ORR activity and stability based on dual transitional metal centers. These consists of two M1 metals with amine nodes coordinated to a single M2 metal with a phthalocyanine linker, where M1/M2 = Co, Ni, or Cu. Co-based metal centers, in particular Ni-Co, demonstrate the highest overall activity of all nine tested MOFs. Computationally, we identify the dominance of Co-sites, relative higher importance of the M2 site, and the role of layer M1 interactions on the ORR activity. Selectivity measurements indicate that M1 sites of MOFs, particularly Co, exhibits lowest (< 4%), and Ni demonstrates highest (>46%) two-electron selectivity, in good agreement with computational studies. Direct in-situ stability characterization, measuring dissolved metal ions, and calculations, using an alkaline stability metric, confirm that Co is the most stable metal in the MOF, while Cu exhibits notable instability at the M1. Overall, this study reveals how atomistic coupling of electronic and structural properties affects the ORR performance of dual site MOF catalysts and opens new avenues for tunable design and future development of these systems for practical electrochemical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An idealized model for the crystal structure of intermetallic compounds isostructural with Mg 3 Cr 2 Al 18

Mg 3 Cr 2 Al 18 (abbreviated in this report as MCA) is the parent phase for a large class of intermetallic compounds that belong to the cubic crystal space group, $Fd\overline{3}m$. The purpose of this paper is to introduce an ideal, unrelaxed crystal structure for compounds isostructural with MCA. There are five distinct atomic sublattices in MCA compounds, which can be denoted, $A, B, C, D,$ and $E$. With this, a general description for MCA structures can be written as $A^{8a}_{1}B^{16c}_{2}C^{16d}_{2}D^{48f}_{6}E^{96g}_{12}$, where the superscripts represent the Wyckoff special equipoints associated with the various sublattices in MCA, and the subscripts indicate the contributions of each sublattice to the stoichiometry of one formula unit in an any given MCA structured compound. Sublattices D and E are where deviations from ideality occur in real, MCA-like compounds. This paper examines MCA bond lengths, nearest-neighbour polyhedral arrangements, 3-D sublattice crystal structures, 2-D atom tessellation patterns, and crystal chemical effects associated with atomic relaxations on the $D$ and $E$ sublattices. The ideal MCA crystal structure developed in this report provides an appropriate initial structure for use as input to crystal structure refinements of diffraction data for MCA-like phases being examined experimentally, or as input for computational, atomistic simulations of the structures of such compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fundamental microscopic properties as predictors of large-scale quantities of interest: Validation through grain boundary energy trends

Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs) provide an avenue to predictive materials discovery. Here, we propose that such correlations can be efficiently discovered through simulations utilizing approximate interatomic potentials (IPs), which serve as an ensemble of “synthetic materials”. As a proof of principle we build a regression model relating canonical properties to the symmetric tilt grain boundary (GB) energy curves in face-centered cubic crystals, characterized by the scaling factor in the universal lattice matching model of Runnels et al. (2016), which we take to be our QoI. Our analysis recovers known correlations of GB energy to other properties and discovers new ones. We also demonstrate, using available density functional theory (DFT) GB energy data, that the regression model constructed from IP data is consistent with DFT results, confirming the assumption that the IPs and DFT belong to same statistical pool and thereby validating the approach. Regression models constructed in this fashion can be used to predict large-scale QoIs based on first-principles data and provide a general method for training IPs for QoIs beyond the scope of first-principles calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Grain boundary relaxation in doped nano-grained aluminum

Here, simulation studies are done to understand the role of dopants that segregate preferentially to grain boundaries on the stability of nanocrystalline aluminum. A dopant design framework based on thermodynamic principles, is used to identify the specific dopant type with the highest potential to segregate to grain boundaries in nanocrystalline aluminum. Various elements are evaluated as potential dopants and magnesium is identified to have the highest tendency to segregate to grain boundaries and release the excess free energyleading to the relaxation of the grain boundaries. A systematic combination of atomic structure analysis is then done to correlate grain boundary relaxation and the mechanical response of the magnesium-doped nanocrystalline aluminum at ambient temperature. The atomistic simulations reveal that the preferential partitioning of magnesium dopants to the grain boundaries reduces the excess volume within this region which stabilizes the nanostructure. At low contents, the magnesium dopants are observed initially partition to the grain boundaries, but once saturation of the grain boundaries is reached, excess dopants are accommodated in the crystalline interiors. It is found that the addition of the magnesium dopants even in the dilute limit, enhances the strength of the nanocrystalline aluminum. The formation of large, disordered GBs in doped nanocrystalline aluminum under tensile load allowed it to accommodate the deformation and prohibit crack growth.

36 MATERIALS SCIENCE↗