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The goal of this work is to develop a Bayesian framework to characterize the uncertainty of material response when using a nonlocal, homogenized model to describe wave propagation through heterogeneous, disordered materials. Our approach is based on an operator regression technique combined with Bayesian optimization, through which the nonlocal kernel for a specific disordered microstructure is investigated.
Here, high-energy reactive mechanical alloying (ball milling) was used to synthesize tetragonal and cubic polymorphs of Cu 2 ZnSnSe 4 . The ordered tetragonal (I-4) polymorph undergoes a phase transition above 400 K into a Cu-Zn disordered tetragonal (I-42 m) polymorph, while the cubic (F-43 m) polymorph with full cation disorder is stabilized at room temperature. Both polymorphs show ultra-low thermal conductivities, 0.42 W m -1 K -1 at 722 K and 0.21 W m -1 K -1 at 523 K for the disordered tetragonal and cubic phases respectively. The cubic polymorph has a higher zT in the low-temperature range, peaking at 0.26 (523 K), while the disordered tetragonal has a maximum zT of 0.46 at 712 K. The latter is the highest reported zT for stoichiometric Cu 2 ZnSnSe 4 , comparable to the best-performing doped materials in the literature. A combination of experimental results and ab-initio calculations point to a coupling between structural disorder and microstructure as the mechanism behind the reported performance.
The blending of glassy copolymer-brush modified colloids with a viscoelastic linear copolymer featuring intrinsic self-healing enabled disordered hyperuniform hybrid materials that combined mechanical robustness with structural color, processability, environmental stability, and the ability to recover structure and properties after incurring physical damage via ‘integrated self-healing’. Symmetric linear n-butyl acrylate/methyl methacrylate (BA/MMA) were co-assembled with asymmetric glassy BA/MMA statistical copolymer brush (silica) particles. ‘Confinement-driven segregation’ resulted in a microphase-separated morphology in which the linear copolymer resided within the interstitial regions of a rigid (∼1 GPa) copolymer brush particle template with disordered hyperuniform microstructure. Diffusion of the self-healing copolymer additive into damage regions drove the recovery after damage, along with the restoration of structural color due to the materials hyperuniform microstructure. The synergistic action of intrinsic and extrinsic healing mechanisms could provide a versatile platform for the bottom-up fabrication of multifunctional hybrid materials with increased damage resistance and functional longevity.
This dataset is comprised of a library of atomistic structure files and corresponding X-ray diffraction (XRD) profiles and vibrational density of states (VDoS) profiles for bulk single crystal silicon (Si), gold (Au), magnesium (Mg), and iron (Fe) with and without disorder introduced into the atomic structure and with and without mechanical loading. Included with the atomistic structure files are descriptor files that measure the stress state, phase fractions, and dislocation content of the microstructures. All data was generated via molecular dynamics or molecular statics simulations using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code. This dataset can inform the understanding of how local or global changes to a materials microstructure can alter their spectroscopic and diffraction behavior across a variety of initial structure types (cubic diamond, face-centered cubic (FCC), hexagonal close-packed (HCP), and body-centered cubic (BCC) for Si, Au, Mg, and Fe, respectively) and overlapping changes to the microstructure (i.e., both disorder insertion and mechanical loading).
Here, the susceptibility to irradiation-induced damage, the fundamental mechanism of the relaxation kinetics, and the corresponding recrystallization effect related to the cation radius ratio are comprehensively investigated for Y 3 Al 5 O 12 and Gd 3 Ga 5 O 12 garnet crystals under 645 MeV Xe 35+ irradiation with different fluences of 5 × 10 11 –3 × 10 12 ions cm –2 . Regarding different lattice distortion and swelling levels, the observed microstructure transformations to disordered and amorphous phases, and corresponding hillock dimensions, consistently confirm that Gd 3 Ga 5 O 12 has a higher susceptibility to radiation damage than Y 3 Al 5 O 12 . Combined with iTS model calculations, although Y 3 Al 5 O 12 has higher atomic temperature and energy deposition than Gd 3 Ga 5 O 12 under the same ion velocity and electronic energy loss, the relatively high thermal conductivity and specific heat coefficient of Y 3 Al 5 O 12 crystals enhance the conduction and dissipation of deposition energy, and Gd 3 Ga 5 O 12 , with a higher cation-radius-ratio (r A /r B ), is more easily damaged to amorphous phase due to the less favorable kinetics of ordering and recovery of a melted track region to the crystalline phase. Additionally, the significant bandgap modification in spectral ranges of 5.88–6.75 eV for Y 3 Al 5 O 12 and 4.83–5.41 eV for Gd 3 Ga 5 O 12 , and the enhancement of defect-assisted-related luminescence are achieved, providing a basis to design novel optoelectronic devices in microelectronics fields.
Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.
High-entropy alloys (HEAs) are materials which leverage the entropy of mixing to motivate the formation of single-phase solid solutions, even of immiscible elements. While these materials are well-recognized for their application to structural engineering, there is increasing interest in the use of HEAs for functional applications such as memory storage and energy devices. The current work investigates the HEA Al 1.3 CoCrCuFeNi, which has been previously shown to be single-phase at high temperatures, but undergoes phase separation at lower temperatures, transforming the structural and the functional properties. This phase separation is investigated at high temperatures with in-situ small angle neutron scattering (SANS) and scanning transmission electron microscopy (EDS). These techniques show that increasing the temperature up to 800°C, the microstructure of the HEA adiabatically disorders and abruptly homogenizes near 700°C, which is consistent with spinodal decomposition. Overall, the microstructural evolution proceeds mainly by the atomistic redistribution of the constituent elements within simple crystal lattices, producing coherent phase mixtures.
Chemical disordering is one feature of damage that occurs in irradiation environments and is common in ceramic compounds. This paper reviews irradiation-induced chemical disordering in SiC-based materials at elevated temperatures. Here the results were obtained using advanced analytical tools to characterize atomistic defects and to evaluate the chemistry of defect clusters and homonuclear bond structures. Finally, this paper demonstrates that chemical disordering is crucial to understanding microstructural stability and establishing a mechanistic modeling of microstructural evolution of irradiated SiC.
Here, the synthesis temperature dependent phase formation of Ni 10 Ti 10 Al 25 Fe 35 Cr 20 thin films is compared to a bulk processed sample of identical composition. The as-cast alloy exhibits a dual-phase microstructure which is composed of a disordered BCC phase and AlNiTi-based B2- and/or L2 1 -ordered phase(s). Formation of the BCC phase as well as an ordered AlNi-based B2 phase is observed for a thin film synthesised at 500 °C (ratio of synthesis temperature of thin film to melting temperature of bulk alloy: T/T m = 0.49), which is attributed to both surface and bulk diffusion mediated growth. Post deposition annealing at 900 °C (T/T m = 0.75) of a thin film deposited without intentional heating results in the formation of NiAlTi-based B2 and/or L2 1 -phase(s) similar to the bulk sample, which is attributed to bulk diffusion. Depositions conducted at room temperature without intentional substrate heating (T/T m = 0.20) resulted in the formation of an X-ray amorphous phase, while a substrate temperature increase to 175 °C (T/T m = 0.28) causes the formation of a BCC phase. Atom probe tomography of the thin films deposited without intentional substrate heating and at 175 °C indicates the formation of ~5 nm and ~10 nm FeAl-rich domains, respectively. This can be rationalized based on the activation energy for surface diffusion, as Ti and Ni exhibt 2.5 to 4 times larger activation energy barriers than Al, Fe and Cr. It is evident from the homologous temperature that the phase formation observed at 500 °C (T/T m = 0.49) is a result of both surface and bulk diffusion. As the temperature is reduced, the formation of FeAl-rich domains can be understood based on the differences in activation energy for surface diffusion and is consistent with kinetically limited thin film growth.
Graphene oxide (GO) membranes intercalated with various organic moieties have shown excellent potential for a range of water processing applications. However, microstructure–functional property relationships in these structurally disordered membranes are not well understood. We demonstrate a practical methodology for developing such relationships for GO membranes intercalated with molecular species, with polycyclic aromatic toluidine blue O (TBO) as an example functional intercalant. We use solid-state UV–vis absorbance and fluorescence measurements to quantitatively track the arrangements of TBO in a series of TBO-loaded reduced GO (rGO) membranes. This study reveals the evolution of diverse arrangements including TBO monomers, lateral and stacked dimers, and other aggregates, as a function of overall TBO loading. These microstructures are then correlated to changes in overall properties such as interlayer d-spacings, permeate fluxes, and solute rejections. The characterization of these different intercalant microstructures explains non-intuitive flux and rejection trends, which can circumvent flux and solute rejection trade-offs.
Abstract Although multiple oxide-based solid electrolyte materials with intrinsically high ionic conductivities have emerged, practical processing and synthesis routes introduce grain boundaries and other interfaces that can perturb primary conduction channels. To directly probe these effects, we demonstrate an efficient and general mesoscopic computational method capable of predicting effective ionic conductivity through a complex polycrystalline oxide-based solid electrolyte microstructure without relying on simplified equivalent circuit description. We parameterize the framework for Li 7- x La 3 Zr 2 O 12 (LLZO) garnet solid electrolyte by combining synthetic microstructures from phase-field simulations with diffusivities from molecular dynamics simulations of ordered and disordered systems. Systematically designed simulations reveal an interdependence between atomistic and mesoscopic microstructural impacts on the effective ionic conductivity of polycrystalline LLZO, quantified by newly defined metrics that characterize the complex ionic transport mechanism. Our results provide fundamental understanding of the physical origins of the reported variability in ionic conductivities based on an extensive analysis of literature data, while simultaneously outlining practical design guidance for achieving desired ionic transport properties based on conditions for which sensitivity to microstructural features is highest. Additional implications of our results are discussed, including a possible connection between ion conduction behavior and dendrite formation.
Underlying the rapidly increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) is the advancement in the understanding of the microstructure of polycrystalline MHP thin film. Over the past decade, intense efforts have been aimed at understanding the effect of microstructures on MHP properties, including chemical heterogeneity, strain disorder, phase impurity, etc. It has been found that grain and grain boundary (GB) are tightly related to lots of microscale and nanoscale behavior in MHP thin films. Atomic force microscopy (AFM) is widely used to observe grain and boundary structures in topography and subsequently to study the correlative surface potential and conductivity of these structures. For now, most AFM measurements have been performed in imaging mode to study the static behavior; in contrast, AFM spectroscopy mode allows us to investigate the dynamic behavior of materials, e.g., conductivity under sweeping voltage. However, a major limitation of AFM spectroscopy measurements is that they require manual operation by human operators, and as such only limited data can be obtained, hindering systematic investigations of these microstructures. Here, we designed a workflow combining the conductive AFM measurement with a machine learning (ML) algorithm to systematically investigate grain boundaries in MHPs. The trained ML model can extract GBs locations from the topography image, and the workflow drives the AFM probe to each GB location to perform a current–voltage (IV) curve automatically. Then, we are able to have IV curves at all GB locations, allowing us to systematically understand the property of GBs. Using this method, we discovered that the GB junction points are less conductive, potentially more photoactive, and can play critical roles in MHP stability, while most previous works only focused on the difference between GB and grains.
Molybdenum disulfide (MoS2) thin films are widely used as dry-film lubricants and protective coatings in aerospace and other radiation-exposed environments. Conventional synthesis routes produce polycrystalline films whose grain boundaries and other native defects cause their mechanical and tribological behavior to differ substantially from that of ideal single crystals. Under irradiation, these films progressively evolve from polycrystalline structures, composed of layered MoS2 grains, into highly disordered and eventually amorphous structures, altering both their tribological performance and mechanical integrity. Here, we employ reactive atomistic simulations to investigate irradiation-driven structural evolution in bulk polycrystalline MoS2. Using controlled primary knock-on atom (PKA) events, we characterize the progressive transition from a polycrystalline microstructure to an amorphous network by tracking defect accumulation and structural disorder. We then establish how this transition modifies the dominant deformation mechanisms and the temperature-dependent tensile response. Specifically, irradiation suppresses interlayer sliding and delamination, mechanisms which facilitate the deformation of the pristine polycrystal, resulting in defect-induced hardening. Broadly, our results establish direct process–structure–property relationships linking irradiation-induced defect accumulation, microstructural evolution, deformation mechanisms, and mechanical behavior, providing an atomistic framework for understanding the structural integrity and long-term reliability of irradiated MoS2 coatings.
Abstract Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.
High entropy alloys (HEAs) have been considered as structural materials for nuclear applications due to their promising mechanical properties and radiation resistance. For this purpose, irradiation-induced defect evolution and microstructure change have been characterized to evaluate the irradiation performance of different HEAs. This article reviews recent advances in understanding the irradiation response of HEAs, including the effect of disordered states on the defect energy landscape and defect evolution, irradiation-induced microstructure changes, void swelling, phase stability, mechanical properties under irradiation, as well as the He irradiation effects.
An electron microscopy investigation was performed to understand the relationship between the microstructure and properties of carbonized cellulose and lignin (softwood kraft lignin) relative to the structure of the original biomass components. Structure details at micro- and molecular levels were investigated by scanning transmission electron microscopy. Atomic-resolution images revealed the presence of random disordered carbon in carbonized cellulose (C-CNC) and of large domains of well-ordered carbon with graphite sheet structure in carbonized lignin (C-Lignin). These structural differences explain why C-CNC exhibits higher surface area and porosity than C-Lignin. The presence of certain well-ordered carbon in carbonized lignin indicates some of the carbon in lignin are graphitized with heat treatment temperature up to 950 °C. This result is encouraging for future endeavors of attaining acceptable modulus of carbon fiber from lignin given suitable modifications to the chemistry and structure of lignin. The results of this research contribute to an improved understanding of the carbonization mechanism of the key cellulose and lignin components of biomass materials.
Superelasticity associated with the martensitic transformation has found a broad range of engineering applications. However, the intrinsic hysteresis and temperature sensitivity of the first-order phase transformation significantly hinder the usage of smart metallic components in many critical areas. In this paper, we report a large superelasticity up to 15.2% strain in [001]-oriented NiCoFeGa single crystals, exhibiting non-hysteretic mechanical responses, a small temperature dependence and high-energy-storage capability and cyclic stability over a wide temperature and composition range. In situ synchrotron X-ray diffraction measurements show that the superelasticity is correlated with a stress-induced continuous variation of lattice parameter accompanied by structural fluctuation. Neutron diffraction and electron microscopy observations reveal an unprecedented microstructure consisting of atomic-level entanglement of ordered and disordered crystal structures, which can be manipulated to tune the superelasticity. The discovery of the large elasticity related to the entangled structure paves the way for exploiting elastic strain engineering and development of related functional materials. NiCoFeGa single crystals exhibit large non-hysteretic superelasticity over broad temperature and composition ranges. It is attributed to the continuous phase transition with applied stress, which is related to the fluctuation of entangled ordered and disordered crystal structures.
Aqueous suspensions of swelling clays display a nematic sol-gel transition at very low solid concentrations. The underlying microstructure of the gel has remained a point of contention since the time of Irving Langmuir and has been a major obstacle to fully realizing the potential of clays for practical applications. Here, we comprehensively probe the microstructure of a smectite clay suspension using ultra-small angle neutron/X-ray scattering and find that the nematic gel is structurally ordered and contains entities that are at least an order of magnitude larger than the individual particles. Complementary cryo-electron microscopy shows the presence of domains having particle-particle ordering responsible for nematic texture and regions of particle-particle aggregation responsible for gel-like behavior. We find that the smectic clay gels have a hybrid microstructure with co-existing repulsive nematic domains and attractive disordered domains.