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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tailoring the Angular Mismatch in MoS 2 Homobilayers through Deformation Fields

Ultrathin MoS 2 has shown remarkable characteristics at the atomic scale with an immutable disorder to weak external stimuli. Ion beam modification unlocks the potential to selectively tune the size, concentration, and morphology of defects produced at the site of impact in 2D materials. Combining experiments, first-principles calculations, atomistic simulations, and transfer learning, it is shown that irradiation-induced defects can induce a rotation-dependent moiré pattern in vertically stacked homobilayers of MoS 2 by deforming the atomically thin material and exciting surface acoustic waves (SAWs). Furthermore, the direct correlation between stress and lattice disorder by probing the intrinsic defects and atomic environments are demonstrated. The method introduced in this paper sheds light on how engineering defects in the lattice can be used to tailor the angular mismatch in van der Waals (vdW) solids.

2D materials↗
DOE OSTI2025

Grain boundary segregation and chemical ordering in CoCrFeMnNi multi-principal element alloy

Owing to their far-from-dilute compositions, multi-principal element alloys (MPEAs) can exhibit unique combinations of engineering properties. As nearly all MPEAs are polycrystalline aggregates, it is necessary to understand the interactions of various elemental species with grain boundaries (GBs). This is of particular importance in extreme environments, such as radiation and elevated temperatures, where such interactions have implications on the properties of MPEAs. Herein, we employ atomistic simulations to generate a series of [001] asymmetric tilt GBs in a model CoCrFeMnNi MPEA and quantify solute interactions and segregation to these boundaries. We employ the Warren-Cowley order parameters to investigate the interplay between GB segregation and chemical short-range order (SRO). At temperatures above 800 K, simulation results reveal the segregation of Cr and Mn to CoCrFeMnNi GBs and show weak dependence of boundary solute excess on GB geometry, at least for the boundaries explored in this work. At temperatures in the range of 673–800 K, formation of domains rich in Cr is observed at GBs in agreement with experimental observations. Quantitative analysis shows that solute excess of various alloying elements decreases rapidly with the increase in temperature in the range of 1000–1200 K. Furthermore, we show that GB regions exhibit SRO characteristics that are distinct from the bulk crystals, leading to spatial variations in SRO. In broad terms, our study highlights the need to account for GB interactions with alloying elements when designing advanced MPEAs with novel chemistries.

Wang, Yitao [Lehigh University, Bethlehem, PA (Uni↗
DOE OSTI2021

A Review: Microstructural and Phase Evolution in Alloys during Extended Plastic Deformation

Materials in service and during processing are often subjected to plastic deformation. For multi-phase metallic alloys, simple geometric models and atomistic simulations suggest that two distinctive regimes in these materials’ evolution during deformation exist. At low strains, evolutions are often dominated by the kinetic roughening of interfaces, which results from the superdiffusive transport of matter in sheared crystals. Furthermore, at high strains and temperatures where thermal diffusion is sluggish, on the other hand, shearing- induced forced atomic mixing dominates these evolutions, resulting in significantly enhanced solubility. Distinguishing these two regimes is shown to provide a convenient framework for rationalizing and analyzing recent experiments and simulations on wear of layered structures in the low strain-regime and nonequilibrium phase co-existence and self-organization in highly immiscible or reactive alloy systems in the high-strain regime.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhanced Radiation Damage Tolerance of Amorphous Interphase and Grain Boundary Complexions in Cu-Ta

Amorphous interfacial complexions are particularly resistant to radiation damage and have been primarily studied in alloys with good glass-forming ability, yet recent reports suggest that these features can form even in immiscible alloys such as Cu-Ta under irradiation. In this work, the mechanisms of damage production and annihilation due to primary knock-on atom collisions are investigated for amorphous interphase and grain boundaries in a Cu-Ta alloy using atomistic simulations. Amorphous complexions, in particular amorphous interphase complexions that separate Cu and Ta grains, result in less residual defect damage than their ordered counterparts. Stemming from the nanophase chemical separation in this alloy, the amorphous complexions exhibit a highly heterogeneous distribution of atomic excess volume, as compared to a good glass former like Cu-Zr. Complexion thickness, a tunable structural descriptor, plays a vital role in damage resistance. Thicker interfacial films are more damage-tolerant because they alter the defect production rate due to differences in intrinsic displacement threshold energies during the collision cascade. Overall, the findings of this work highlight the importance of interfacial engineering in enhancing the properties of materials operating in radiation-prone environments and the promise of amorphous complexions as particularly radiation damage-tolerant microstructural features.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Carbon nanotube (CNT) metal composites exhibit greatly reduced radiation damage

Radiation damage of structural materials leads to mechanical property degradation, eventually inducing failure. Secondary-phase dispersoids or other radiation defect sinks are often added to materials to boost their radiation resistance. We demonstrate that a metal composite made by adding 1D carbon nanotubes (CNTs) to aluminum (Al) exhibits superior radiation resistance. In situ ion irradiation with transmission electron microscopy (TEM) and atomistic simulations together reveal the mechanisms of rapid defect migration to CNTs, facilitating defect recombination and enhancing radiation tolerance. The origin of this effect is an evolving stress gradient in the Al matrix resulting from CNT transformation under irradiation, and the stability of resulting carbides. Extreme value statistics of large defect behavior in our simulations highlight the role of CNTs in reducing accumulated damage. Furthermore, this approach to controlling defect migration represents a promising opportunity to enhance the radiation resistance of nuclear materials without detrimental effects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Three-dimensional atomic scale characterization of {11$\overline{2}$2} twin boundaries in titanium

The {11$\overline{2}$2}<11$\overline{23}$> compression twin can accommodate a considerable amount of strain under c-axis compression in Ti. However, unlike the tensile twin, the structure of the {10$\overline{1}$2}$\langle$$\overline{1}$011$\rangle$ compressive twins has not been completely characterized. In this study, we apply a combined technique of HR-TEM characterization, topological analysis, and atomistic simulations to explore the facets that bound the {11$\overline{2}$2}<11$\overline{23}$> twin in Ti. In addition to the currently known facets (CTB and B-Py), six new facets are observed and categorized for the first time from atomic-scale TEM observations along five crystallographic directions. The six new facets are (11$\overline{2}$0)//(11$\overline{2}$6), PrPr1, PyPy1, (2$\overline{11}$1)//($\overline{1}$2$\overline{1}$2), (1$\overline{1}$04)//(01$\overline{11}$), and (01$\overline{1}$0)//(2$\overline{11}$4). Results from the topological and computational analysis are in reasonable agreement with and support the HRTEM observations. Specifically, (1) the observed facets align with low-index interfaces in both twin and matrix domains, (2) the facets with lower surface energies are found to form extended interfaces, and (3) high-surface-energy facets are found at the twin tip region and explained by the fact that the energy of the combined facet and facet junction configuration is energetically preferred in the twin tip region. These results not only provide a comprehensive understanding of the 3D structure of the {11$\overline{2}$2}<11$\overline{23}$> compressive twins in Ti, but also validate the MD procedure and the Ti interatomic potential employed. This is extremely important for future study of the {11$\overline{2}$2} twin mobility and interactions with other defects, both features that remain extremely challenging to capture in experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Grain boundary softening from stress assisted helium cavity coalescence in ultrafine-grained tungsten

The formation of helium cavities in coarse-grained materials produces hardening proportional to the number density and size of the cavities and due to the interaction of dislocations with intragranular helium defects. In nanostructured metals containing a high density of interfacial sinks, preferential cavity formation in the grain boundaries instead produces softening that is often attributed to enhanced interfacial plasticity. Here, employing two grades of ultrafine-grained tungsten, we explore this effect using targeted implantation studies to map cavity evolution as a function of the irradiation conditions and quantify its impact on the mechanical response through nanoindentation. Softening is reported at implantation temperatures above the threshold for preferential grain boundary cavity formation but at a sufficiently low fluence prior to the growth of intragranular cavities. Collective changes in the mean cavity size, density, and morphology beneath a residual impression on an implanted surface indicate that cavity coalescence accompanied the reduction in hardness. Complementary atomistic simulations demonstrate that, in tungsten grain structures exhibiting softening, grain boundary bubble coalescence is driven by stress concentrations that further act to localize strain in the grain boundaries through cooperative deformation processes involving local atomic shuffling and sliding, dislocation emission, and even the nucleation of unstable twinning events.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Effect of Grain Boundary Facet Junctions on Segregation and Embrittlement

Junctions are discontinuities in flat grain boundaries that arise in all polycrystalline materials and are thought to play important roles in the response of a grain boundary network to thermal and mechanical loads. A key open question concerns the mechanisms by which solute segregation to junctions impacts properties of the grain boundary. Here, in this work, we investigate the influence of grain boundary facet junctions on solute embrittlement, and we present an analytical model that uses the hydrostatic stress field contributed by dislocations at multiple junctions to describe these effects. Specifically, we study junctions between {112} facets of various lengths in Au $\langle111\rangle$ Σ3 tilt grain boundaries. Copper and silver solutes are employed to determine if the effect of junctions on solute segregation and embrittlement is dependent on size relative to the host. Combined, atomistic simulation data and the analytical model show that Cu and Ag have opposite segregation responses to junctions due to the sign of the hydrostatic stress field induced by junctions. However, a positive shift in the embrittling potency is computed near junctions regardless of solute type or the stress state of the segregation site. Hence, for the conditions studied, junctions consistently shift the energetic landscape towards embrittlement.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ultimate compressive strength and severe plastic deformation of equilibrated single-crystalline copper nanoparticles

Mechanical properties and deformation mechanisms of defect-free copper nanoparticles are investigated by combining experiments with atomistic simulations. The compressive strength of the particles increases with decreasing size and tends to saturate near the theoretical strength in the small-size limit. In this limit, the intrinsic size dependence of the strength is governed by the stochastic nature of dislocation nucleation near the particle surface. The particle deformation process evolves from the initial strain softening to strain hardening as the particle accumulates residual damage. The normalized strength-size relation for Cu is compared with those for Au, Ni, and Pt. The lack of universal behavior among the four FCC metals is discussed. Heavily deformed Cu nanoparticles develop polycrystalline structures and change the lattice orientation from [111] to [110]. The experiments and simulations reveal the twinning mechanism of the lattice rotation leading to the new grain formation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Formation of carbon homonuclear bonds in β-SiC under neutron irradiation at various temperatures and neutron doses

To elucidate radiation defect processes in SiC, Raman spectroscopy was systematically applied to high-purity, polycrystalline β-SiC that was neutron irradiated at a range of temperature and dose conditions. The analysis specifically focused on formation of carbon homonuclear bonds by irradiation; these bonds were indicated by D and G bands and amorphous carbon peaks. Intensity of the carbon peaks relative to SiC peaks significantly decreased in the case of high temperature and/or high neutron dose of 500 °C to 29 displacements per atom (dpa) and about 800 °C to 1.38 and 29 dpa. The absence of carbon bond peaks under those conditions was explained by growth of stoichiometric defect clusters, consistent with previous atomistic simulations on SiC defect stability. The lack of Raman bands associated with carbon clusters under high-temperature and high-dose radiation conditions accounts for the resistance of SiC to phase separation under irradiation. The findings further suggest that material compositions and chemical properties that are inherently resistant to chemical disordering under high-dose radiation conditions are indicative of the long-term durability of ceramic compounds in radiation environments.

36 MATERIALS SCIENCE↗
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

Polymer-cement composites with adhesion and re-adhesion (healing) to casing capability for geothermal wellbore applications

Deterioration of cement/casing adhesion in wellbore scenarios can result in unwanted and potentially harmful leakage with the potential of serious repair costs. In this work, we explore the use of self-healing polymers added to conventional wellbore cements as a way to bring about self-healing and readhering (to steel casing) properties to the composite material. The polymers are pH resistant and seem to improve the cement integrity after exposure to typical chemical and thermal stresses encountered under geothermal wellbore conditions. We find that addition of about 10-15 wt% of polymer to the cement visually increases its resistance to fracturing from exposure to geothermal conditions, while the adhesive strength of cement/stainless steel increases with curing time for a period of about 10 days. Self-healing capability was demonstrated by permeability analysis showing that polymer-cement composites reduce flow by 50-70% at cement bulk and at the cement/steel interface. Use of atomistic simulations imply that these polymers have good wetting properties on the steel surfaces. Analysis of the interactions between steel/polymer and cement/polymer show that they are complementary, resulting in a wider range of bonding patterns. Cracks are likely to expose under-coordinated sites that result in more bonding interactions, which agrees well with the permeability measurements showing high degree of healed cracks and healed (cement-steel) interfacial gaps together with an overall increased in structural integrity of these advanced polymer-cement composite materials.

Rod, Kenton A.↗
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

A high-throughput workflow to analyze sequence-conformation relationships and explore hydrophobic patterning in disordered peptoids

Understanding how a macromolecule’s primary sequence governs its conformational landscape is crucial for elucidating its function, yet these design principles are still emerging for macromolecules with intrinsic disorder. Herein, we introduce a high-throughput workflow that implements a practical colorimetric conformational assay, introduces a semi-automated sequencing protocol using matrix-assisted laser desorption/ionization and tandem mass spectrometry (MALDI-MS/MS), and develops a generalizable sequence-structure algorithm. Using a model system of 20mer peptidomimetics containing polar glycine and hydrophobic N-butylglycine residues, we identified nine classifications of conformational disorder and isolated 122 unique sequences across varied compositions and conformations. Conformational distributions of three compositionally identical library sequences were corroborated through atomistic simulations and ion mobility spectrometry coupled with liquid chromatography. A data-driven strategy was developed using existing sequence variables and data-derived “motifs” to inform a machine-learning algorithm toward conformation prediction. Here, this multifaceted approach enhances our understanding of sequence-conformation relationships and offers a powerful tool for accelerating the discovery of materials with conformational control.

data-driven analysis↗
DOE OSTI2021

Phase-field modeling of the interactions between an edge dislocation and an array of obstacles

Obstacles, such as voids and precipitates, are prevalent in crystalline materials. They strengthen crystals by serving as barriers to dislocation glide. Here in this work, we develop a phase-field dislocation dynamics (PFDD) technique for investigating the interactions between dislocations and second-phase obstacles, which can be either voids or precipitates. The PFDD technique is constructed to account for elastic heterogeneity, elastic anisotropy, dissociation of the dislocation, and dislocation transmission across bicrystalline interfaces. Within the framework, we present a model for “pseudo-voids”, which are voids shearable by dislocations, in contrast to unphysical, unshearable voids in conventional phase-field dislocation formulations. We employ the PFDD technique to investigate the in-plane interactions between an edge dislocation and an array of nano-scale obstacles with different spacings. In this application, the interactions take place in glide planes of either a face-centered cubic (FCC) Cu or a body-centered cubic (BCC) Nb matrix, while the precipitates have a Cu 1-x Nb x composition, with x varying from 0.1 to 0.9. Our atomistic simulations find that the alloy precipitates can have an FCC, an amorphous, or a BCC phase, depending on the compositional ratio between Cu and Nb, i.e., value of x. Among all types of obstacles, the critical stresses for dislocation bypass are the highest for unshearable amorphous precipitates, followed by shearable crystalline precipitates, and then the pseudo-voids.

42 ENGINEERING↗
DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A machine learning approach to predict thermal expansion of complex oxides

Although it is of scientific and practical importance, the state-of-the-art of predicting the thermal expansion of oxides over broad temperature and composition ranges by physics-based atomistic simulations is currently limited to qualitative agreements. We present an emerging machine learning (ML) approach to accurately predict the thermal expansion of cubic oxides with a dataset consisting of experimentally measured lattice parameters while using the metal cation polyhedron and temperature as descriptors. High-fidelity ML models that can accurately predict temperature- and composition-dependent lattice parameters of cubic oxides with isotropic thermal expansions have been successfully trained. The ML-predicted thermal expansions of oxides not included in the training dataset have shown good agreement with available experiments. The limitations of the current approach and challenges to go beyond cubic oxides with isotropic thermal expansion are also briefly discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning enabled discovery of superhard and ultrahard carbon polymorphs

The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.

Balasubramanian, Karthik [Univ. of Illinois, Chica↗