Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN (Atomistic Line Graph Neural Network) encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.
Spin chains have long been considered an effective medium for long-range interactions, entanglement generation, and quantum state transfer. In this work, we explore the properties of a spin chain implemented with superconducting flux circuits, designed to act as a connectivity medium between two superconducting qubits. The susceptibility of the chain is probed and shown to support long-range, cross-chain correlations. In addition, interactions between the two end qubits, mediated by the coupler chain, are demonstrated. This work has direct applicability in near term quantum annealing processors as a means of generating long-range, coherent coupling between qubits.
High entropy alloys (HEAs) are promising materials for various applications including nuclear reactor environments. Thus, understanding their behavior under irradiation and exposure to different environments is important. Here, two sets of near-equiatomic CoCrCuFeNi thin films grown on either SiO 2 /Si or Si substrates were irradiated at room temperature with 11.5 MeV Au ions, providing similar behavior to exposure to inert versus corrosion environments. The film grown on SiO 2 had relatively minimal change up to peak damage levels above 500 dpa, while the film grown on Si began intermixing at the substrate–film interface at peak doses of 0.1 dpa before transforming into a multi-silicide film at higher doses, all at room temperature with minimal thermal diffusion. The primary mechanism is radiation-enhanced diffusion via the inverse Kirkendall and solute drag effects. The results highlight how composition and environmental exposure affect the stability of HEAs under radiation and give insights into controlling these behaviors.
A detailed experimental and kinetic modeling study was dedicated to understand the reported octane hyperboosting effect of prenol, by means of the measurement of the ignition delay times of its blends with iso-octane, and measurement of the mole fraction profiles of the fuels and intermediates inside the ULille rapid compression machine. These results show that prenol addition leads to a reduction of the first-stage ignition phenomena and negative temperature coefficient behavior, which is only qualitatively captured by the model and is consistent with knock resistance improvement. It is suggested that this behavior is caused by two different factors. The first originates from gas-phase reactivity of prenol, and spans from the formation of unreactive unsaturated species through resonance-stabilized radicals, thereby constituting a competitive pathway for the radical pool generated by iso-octane. The second is of catalytic nature and cannot be captured by means of gas-phase kinetic modeling, but could also play an important role in the behavior of prenol in internal combustion engines.
We introduce a new computational methodology for the identification and characterization of free volume within/around atomistic configurations. This scheme employs a three-stage workflow, by which spheres are iteratively grown inside of voxels, and ultimately converted to planar graphs, which are then characterized via a graph-based order parameter. Our approach is computationally efficient, physically intuitive, and universally transferable to any material system. Validation of our methodology is performed on several sets of materials problems: (1) classification of unique free volumes in various crystal phases, (2) autonomous detection and classification of complex surface defects during epitaxial growth simulations, (3) characterization of free volume defects in metals/alloys, and (4) quantification of the spatio-temporal behavior of nano-scale free volume morphologies as a function of both temperature and free-volume size. Our method accurately identifies and characterizes unique free volumes over a multitude of systems and length scales, indicating its potential for future use in understanding the relationship between free volume morphology and material properties under both static and dynamic conditions.
Understanding the deformation-induced martensitic transformation (DIMT) is critical for interpreting the structure-property relationships that govern the performance of transformation-induced plasticity (TRIP) assisted steels. However, modern TRIP-assisted steels often exhibit DIMT kinetics that are not easily captured by existing empirical models based on bulk tensile strain. We address this challenge by combined bulk uniaxial tensile tests and in-situ high energy synchrotron X-ray diffraction, which resolved the phase volume fractions, stress-strain response, and microstructure evolution of each constituent phase. A modification of the Olson-Cohen model is implemented, which describes the martensitic transformation kinetics as a function of the estimated partitioned strain in austenite, rather than the bulk tensile strain. This DIMT kinetic model is used as a framework to clarify the root cause of an insufficiently understood toughness trough reported for TRIP-assisted steels during deformation at elevated temperatures. The importance of the temperature-dependent toughness is discussed, based on the opportunity to modify deformation processes to tailor the DIMT kinetics and mechanical properties during forming and in service.
Kinetic treatment of the full group of C5 olefins is presented with new measurements on 1-pentene (1-C5H10), 2-pentene (2-C5H10), and 3-Methyl-1-Butene (3M1B) combined with recently published data obtained at similar conditions from our group on 2-Methyl-2-Butene (2M2B) and 2-Methyl-1-Butene (2M1B). This extensive experimental database contains carbon monoxide and water time-history profiles, along with their measured CO and H2O induction delay times. The oxidation of the five pentene isomers was carried out at three equivalence ratios (0.5, 1.0, and 2.0) in mixtures highly diluted in 99.5% Helium-Argon. The experiments were performed for temperatures ranging from 1400 to 1900 K at near-atmospheric pressure. A unique comparison of the complete set of pentene isomers permits the understanding of the C=C double bond position and branching impacts on combustion properties, using the chemical kinetics mechanism of both linear and branched structures. The impact of the C = C double bond location – either the 1–2 or 2–3 bond site – is described using the linear molecules 1-C5H10 and 2-C5H10. Species induction delay times were measured for the five isomers for each equivalence ratio investigated. Results showed noticeable differences between isomers, with the induction delay time results for 3M1B being the shortest, closely followed by 2-C5H10, 1-C5H10, and then after a large leap in decreasing reactivity, by 2M2B and 2M1B. Numerical predictions using up to 9 models available in the literature were performed. An error score function was used to evaluate the properties of the pentene isomer models in the current literature.
During the conversion of nuclear waste feed into glass, iron bearing precursors are incorporated into the melt and affect various properties of the melt such as viscosity and density. Laboratory-scale measurements to determine iron oxidation state could be used in cases where samples are subjected to ex-situ heat treatment and kinetic relationships developed. Typically, measurements of iron oxidation state have been limited to synchrotron X-ray absorption techniques, Mossbauer spectroscopy, or destructive wet chemical techniques. In this work, we present our method development which employs electron probe microanalysis (EPMA) to measure the oxidation state of iron. This method could also serve broader applications, not only in nuclear waste vitrification but also in commercial glass making, geology, and archaeology. Using EPMA, we measured the Fe L-edge X-ray emission spectra of nuclear waste feed samples prepared with a reduced iron precursor and heat treated to various temperatures in air. EPMA based measurements provide a cost-effective and rapid alternative to measure iron oxidation state. Furthermore, the method described in this paper allows for spatially resolved measurements with a minimum step size of 100 µm. We demonstrate that iron oxidation state can be calculated using EPMA, and we compare the calculated iron oxidation state to that measured by synchrotron Fe K-edge X-ray absorption measurements. This work demonstrates that, for nuclear waste feeds prepared with reduced iron, the oxidation state of the iron is influenced by the thermal history. Fast-drying the feed slurry produced more reduced iron in feed than previously observed in feeds prepared by slowly drying and crushing into a powder.
Clean energy heating electrification programs provide a promising way to reduce carbon emissions from fossil fuel combustion and consumption. This work studies the cost competitiveness of clean energy heating technologies under three dynamic mechanisms: investment costs, subsidy policies, and operating costs with real data. It provides key insights into the cost competitiveness of the different heating technologies deployed in different areas, as well as their sensitivity to the three dynamic mechanisms. The results show that currently, the distinct heating programs are more cost-efficient in the urban area with existing heating networks. The average payback period of all district clean energy heating programs in the urban area is 14.9 years, while that of the individual clean heating programs is 24.7 years. The individual heating programs are becoming increasingly cost-competitive with the incentive mechanisms, especially the electricity pricing mechanisms. Moreover, individual heating technologies present remarkable advantages on flexibility and sustainability in the long run. According to the technology diffusion model proposed in this paper, the individual clean heating programs will occupy more than 50% of the market share in 2050 under the comprehensive effect of CAPEX, government subsidies, and OPEX. The real-world results and analysis render references to shape the pathway of clean energy heating electrification in Northwest China and other regions with a similar situation.
To better understand the effects of infiltration on local electrochemistry and transport in solid oxide fuel cell (SOFCs) electrodes, high-throughput, high-performance finite element simulations are presented within dozens of SOFC cathodes containing synthetically generated nanoscale infiltrates. The computational approach retains the complex microstructural morphologies of cathodes, including those of the three backbone phases (gas, ion, and electron conductors) and the infiltrates (an electron conductor), in meshed domains and computes distributions of local electrochemical quantities within the domains. Simulations were implemented on a supercomputer and converged for 48 distinct microstructural subvolumes, with varying backbone heterogeneities and infiltrate loadings. Analyzing both the ensemble (averaged over subvolumes) and the local (evaluated within subvolumes) performance metrics indicate that infiltration of an electron conductor significantly improves the electrochemical performance of each backbone in a linear fashion with the increase of triple phase boundary content, but the essential ionic transport pathways of the backbone are unchanged. These results shed light into the design and fabrication of optimal electrodes in fuel cells.
River discharge is one of the most critical renewable water resources. Accurately estimating river discharge with land surface models (LSMs) remains challenging due to the difficulty in estimating land water storages such as snow, soil moisture, and groundwater. While data assimilation (DA) ingesting optical, microwave, and gravity measurements from space can help constrain theses storage states, its impacts on runoff and eventually river discharge are not fully understood. Here, by taking advantage of recently published land DA results that jointly assimilate eight different combinations of observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), Gravity Recovery and Climate Experiment (GRACE), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), we quantify to what degree multi-sensor land DA improves the river discharge simulation skills over 40 global river basins, and investigate the complementary strengths of different satellite measurements on river discharge. To be more specific, river discharge is updated by feeding gridded runoff from the eight multi-sensor DA simulations into a vector-based river routing model named the Routing Application for Parallel computatIon of Discharge (RAPID). Our modeling results, including 7-year simulations at 177,458 river reaches globally, are used to study the seasonal to interannual variability of river discharge. It is found that assimilating GRACE has the greatest impact on global runoff patterns, leading to the most pronounced improvements in spatial river discharge in the middle and high latitudes with the R² increased by 0.16. The seasonal variation of spatial discharge is most skillful during the boreal summer. However, our evaluation also shows model and DA still struggle to generate reasonable variability and averaged discharge over permafrost regions. By assessing how different satellites add value to discharge forecasts, this study paves the way for more advanced multi-sensor satellite data assimilation to predict the terrestrial hydrological cycle.
Calcium aluminosilicate hydrate (C-A-S-H) is the binding phase of both blended cement-based and alkali-activated materials. The intrinsic mechanical properties of non-cross-linked C-A-S-H are important while experimentally unvalidated. Here, the properties are for the first time measured using high-pressure X-ray diffraction. The incompressibility and bulk modulus K0 of C-A-S-Hs are correlated to their nanostructure and stability using nuclear magnetic resonance and X-ray absorption spectroscopies. Al coordination in stable C-A-S-H (Al/Si = 0.1) cured for 546 days is purely tetrahedral (AlIV), while in metastable C-A-S-H (Al/Si = 0.05) cured for only 182 days is both AlIV and pentahedral (AlV). The stable C-A-S-H is stiffer along the a,b,c-axis with higher K0 relative to C-S-H. Short-curing-induced metastable C-A-S-H (Al/Si = 0.05) shows expanded interlayer and softer c-axis, thus lower K0 than C-S-H and the stable C-A-S-H. Our results highlight the stiffening effect of AlIV incorporation and the negative influences of insufficient curing on the nanomechanical properties of non-cross-linked C-A-S-H at Ca/Si = 1.
Additive manufacturing produces net-shaped components layer by layer for engineering applications1–7. The additive manufacture of metal alloys by laser powder bed fusion (L-PBF) involves large temperature gradients and rapid cooling2,6, which enables microstructural refinement at the nanoscale to achieve high strength. However, high-strength nanostructured alloys produced by laser additive manufacturing often have limited ductility3. Here we use L-PBF to print dual-phase nanolamellar high-entropy alloys (HEAs) of AlCoCrFeNi2.1 that exhibit a combination of a high yield strength of about 1.3 gigapascals and a large uniform elongation of about 14 per cent, which surpasses those of other state-of-the-art additively manufactured metal alloys. The high yield strength stems from the strong strengthening effects of the dual-phase structures that consist of alternating face-centred cubic and body-centred cubic nanolamellae; the body-centred cubic nanolamellae exhibit higher strengths and higher hardening rates than the face-centred cubic nanolamellae. The large tensile ductility arises owing to the high work-hardening capability of the as-printed hierarchical microstructures in the form of dual-phase nanolamellae embedded in microscale eutectic colonies, which have nearly random orientations to promote isotropic mechanical properties. The mechanistic insights into the deformation behaviour of additively manufactured HEAs have broad implications for the development of hierarchical, dual- and multi-phase, nanostructured alloys with exceptional mechanical properties. An additive manufacturing strategy is used to produce dual-phase nanolamellar high-entropy alloys that show a combination of enhanced high yield strength and high tensile ductility.
The free energy involved in the formation of an interface between two phases (e.g., a solid-liquid interface) is referred to as the interfacial free energy. For the case of solidification, the interfacial free energy dictates the height of the energy barrier required to nucleate stable clusters of the newly forming solid phase and is essential for producing an accurate solidification kinetics model using classical nucleation theory (CNT)-based methods. While various methods have been proposed for modeling the interfacial free energy for solid-liquid interfaces in prior literature, many of these formulations involve making restrictive assumptions or approximations, such as the system being at or near equilibrium (i.e., the system temperature is approximately equal to the melt temperature) or that the system is at pressures close to atmospheric. However, these approximations and assumptions may break down in highly non-equilibrium situations, such as in dynamic-compression experiments where metastable liquids that are undercooled by hundreds of kelvin or overpressurized by several gigapascals or more are formed before eventually solidifying. We derive a solid-liquid interfacial free-energy model for such high-pressure conditions by considering the enthalpies of interactions between pairs of atoms or molecules. We also consider the contribution of interface roughness (disordering) by incorporating a multilayer interface model known as the Temkin n-layer model. Our formulation is applicable to a diverse variety of materials, and we demonstrate it by developing models specifically for two different materials: water and gallium. We apply our interfacial free-energy formulation to CNT-based kinetics simulations of several suites of dynamic-compression experiments that cause liquid water to solidify to the high-pressure solid polymorph ice VII and have found good agreement to the observed kinetics with only minor empirical fitting.
We develop several inference methods to estimate the position of dislocations from images generated using dark-field X-ray microscopy (DFXM)—achieving superresolution accuracy and principled uncertainty quantification. Using the framework of Bayesian inference, we incorporate models of the DFXM contrast mechanism and detector measurement noise, along with initial position estimates, into a statistical model coupling DFXM images with the dislocation position of interest. We motivate several position estimation and uncertainty quantification algorithms based on this model. We then demonstrate the accuracy of our primary estimation algorithm on synthetic realistic DFXM images of edge dislocations in single-crystal aluminum. We conclude with a discussion of our methods’ impact on future dislocation studies and possible future research avenues.
The energetics of the regioselective mononitration of 9,10-BN-naphthalene with acetyl nitrate (H3C2NO4) were modeled with ab initio simulations in the gas phase and an acetonitrile solvent. The single-electron-transfer (SET) nitration mechanism leading to a σ-complex and a single-step nitration mechanism were modeled. The energy barrier for the single-step mechanism was lower than that for the SET mechanism in the gas phase. However, the two are much more energetically competitive in the solvent. The σ-complex was found to be unstable in the gas phase owing to the interaction with the counterion. Using the single-step mechanism, the carbon site 1 nearest boron had the lowest activation energy for nitration of 22.6 kcal/mol, while site 3 had the second lowest barrier of 24.6 kcal/mol. Details on the molecular structures at intermediate and transition states as well as charges in different configurations are discussed.
We present an hr-adaptivity framework for optimization of high-order meshes. This work extends the r-adaptivity method by Dobrev et al. (Comput Fluids, 2020), where we utilized the Target-Matrix Optimization Paradigm (TMOP) to minimize a functional that depends on each element’s current and target geometric parameters: element aspect-ratio, size, skew, and rotation. Since fixed mesh topology limits the ability to achieve the target size and aspect-ratio at each position, in this paper, we augment the r-adaptivity framework with nonconforming adaptive mesh refinement to further reduce the error with respect to the target geometric parameters. The proposed formulation, referred to as hr-adaptivity, introduces TMOP-based quality estimators to satisfy the aspect-ratio target via anisotropic refinements and size target via isotropic refinements in each element of the mesh. The methodology presented is purely algebraic, extends to both simplices and hexahedra/quadrilaterals of any order, and supports nonconforming isotropic and anisotropic refinements in 2D and 3D. Using a problem with a known exact solution, we demonstrate the effectiveness of hr-adaptivity over both r- and h-adaptivity in obtaining similar accuracy in the solution with significantly fewer mesh nodes. We also present several examples that show that hr-adaptivity can help satisfy geometric targets even when r-adaptivity fails to do so, due to the topology of the initial mesh.
Engineering thermophysical properties of metal hydrides nanocomposites is crucial for effective thermal management during hydrogen storage reactions; however, the effect of microstructure on thermal transport mechanisms is still unclear. Here, we employed an integrated experiment-modeling approach to investigate microstructural factors that determine the effective thermal conductivity of individual reduced graphene oxide-magnesium (rGO/Mg) nanocomposites and their packed bed. The effective thermal conductivity of the rGO/Mg nanocomposite packed bed was measured by using guarded hot-plate method under various atmospheric conditions (i.e., vacuum, Ar and He). A microstructure-aware mesoscopic modeling revealed that anisotropy of the effective thermal conductivity of individual rGO/Mg nanocomposites plays an important role in determining the effective thermal conductivity of their packed bed. The validated mesoscopic model also disclosed a nontrivial interplay between the intrinsic rGO properties and the extrinsic composite structural features. Finally, quantitative sensitivity analysis based on the modeling framework is used to provide practical engineering guidance for controlling the thermal transport within nanocomposite packed beds.
High-entropy alloys (HEAs) and some complex alloys exhibit desirable properties and significant structural stability in harsh environments, including possible applications in advanced reactors. Energetic ion irradiation is often used as a surrogate for neutron irradiation; however, the impact of ion electronic energy deposition and dissipation is often neglected. Moreover, differences in recoil energy spectrum and density of cascade events on damage evolution must also be considered. In many chemically complex alloys, the mean free path of electrons is reduced significantly, thus their decreased thermal conductivity and slow dissipation of localized radiation energy can have noticeable effects on displacement cascade evolution that is greatly different from metals with high thermal conductivity. In this work, nanocrystalline HEAs of Ni 20 Fe 20 Co 20 Cr 20 Cu 20 and nonequiatomic (NiFe-CoCr) 97 Cu 3 , both having much lower room-temperature thermal conductivity than pure Ni or Fe, are chosen as model HEAs to reveal the role that electronic energy loss during ion irradiation has in complex alloys. The response of nanocrystalline HEAs is investigated under irradiation at room temperature using MeV Ni and Au ions that have different ratios of electronic energy to damage energy, which is the energy dissipated in displacing atoms. Different from previously reported amorphization of nanocrystalline SiC, experimental results on these HEAs show that, similar to the process in nanocrystalline oxide materials, both inelastic thermal spikes via electron-phonon coupling and elastic thermal spikes via collisions among atomic nuclei contribute to the overall grain growth. The growth follows a power law dependence with the total deposited ion energy, and the derived value of the power-exponent suggests that the irradiation-induced instability at and near grain boundaries leads to local rapid atomic rearrangements and consequently grain growth. The high power-exponent value can be attributed to the sluggish diffusion and delayed defect evolution arising from the chemical complexity intrinsic to HEAs. This work calls attention to quantified fundamental understanding of radiation damage processes beyond that of simplified displacement events, especially in simulating neutron environments.
Polycyclic aromatic hydrocarbons (PAHs) are important intermediates to soot formation in combustion. A reliable database of their thermochemistry is required for the development of chemical kinetic models describing the gas-phase chemistry of hydrocarbon fuels. In this study, temperature-dependent thermodynamic properties are consistently determined using high-accuracy quantum chemistry calculations for an extensive set of PAH compounds. The developed database comprehensively consists of 125 C6-C18 PAH molecules and radicals, which are important and commonly included in chemical mechanisms studies. At the M06-2X/6-311++G(d,p) level of theory, geometry optimizations, vibrational frequency calculations, and dihedral angle scans are performed for all PAH species. The G3 method, together with the atomization reaction approach, is selected to derive the average atomization formation enthalpy. This method produces the most accurate thermochemistry quantities for PAHs, as demonstrated in a previous study. The entropy and heat capacity values are calculated using statistical thermodynamics in MultiWell. These results exhibit good agreement with the databases in literature. To examine the application of the group additivity (GA) method for PAHs, the Bland−Altman plot, a statistical analysis approach, is employed to visualize the agreement between the results from the quantum chemical calculations and GA methods. Two GA methods are examined and significant differences are found, which indicates that GA values of relevant groups need to be further updated. The database of thermodynamic quantities developed in this study are of particular value in modeling studies and important for exploring mechanisms of the PAH growth.
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