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Results for “configurational complexity”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Applying Configurational Complexity to the 2D Ruddlesden–Popper Crystal Structure

The layered Ruddlesden–Popper crystal structure can host a broad range of functionally important behaviors. In this work, we establish extraordinary configurational disorder in a layered Ruddlesden–Popper (RP) structure using entropy stabilization assisted synthesis. A protype A 2 CuO 4 RP cuprate oxide with five cations on the A-site sublattice is designed and fabricated into epitaxial single crystal films using pulsed laser deposition. When grown on a near lattice matched substrate, the (La 0.2 Pr 0.2 Nd 0.2 Sm 0.2 Eu 0.2 ) 2 CuO 4 film features a T'-type RP structure with uniform A-site cation mixing and square-planar CuO4 units. These observations are made with a range of combined characterizations using X-ray diffraction, atomic-resolution scanning transmission electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray absorption spectroscopy measurements. It is further found that heteroepitaxial strain plays an important role in crystal phase formation during synthesis. Compressive strain over ~1.5% results in the formation of a non-RP cubic phase consistent with a Cu X 2O 4 spinel structure. The ability to manipulate configurational complexity and move between 2D layered RP and 3D cubic crystal structures in cuprate and related materials promises to enable flexible design strategies for a range of functionalities, such as magnetoresistance, unconventional superconductivity, ferroelectricity, catalysis, and ion transport.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Unveiling the complex configurational landscape of the intralayer cavities in a crystalline carbon nitride

The in-depth understanding of the reported photoelectrochemical properties of the layered carbon nitride, poly(triazine imide)/LiCl (PTI/LiCl), has been limited by the apparent disorder of the Li/H atoms within its framework. To understand and resolve the current structural ambiguities, an optimized one-step flux synthesis (470 °C, 36 h, LiCl/KCl flux) was used to prepare PTI/LiCl and deuterated-PTI/LiCl in high purity. Its structure was characterized by a combination of neutron/X-ray diffraction and transmission electron microscopy. The range of possible Li/H atomic configurations was enumerated for the first time and, combined with total energy calculations, reveals a more complex energetic landscape than previously considered. Experimental data were fitted against all possible structural models, exhibiting the most consistency with a new orthorhombic model (Sp. Grp. Ama2) that also has the lowest total energy. In addition, a new Cu(I)-containing PTI (PTI/CuCl) was prepared with the more strongly scattering Cu(I) cations in place of Li, and most closely matching with the partially-disorder structure in Cmc2 1 . Thus, a complex configurational landscape of PTI is revealed to consist of a number of ordered crystalline structures that are new potential synthetic targets, such as with the use of metal-exchange reactions.

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

Expanding Configurational Complexity through Dipole Dilution in Pseudohalide Argyrodite Ion Conductors

The advantageous properties of (pseudo)halide argyrodite ion conductors of the formula Li 6 PS 5 X (X = Cl – , Br – , I – , CN – ) have motivated extensive studies of their structure-transport relationships, particularly as they pertain to the role of atomic site disorder. The argyrodite structure can accommodate additional configurational complexity to promote ion transport via extended three-anion site mixing and the potential for orientational disorder of molecular anions. In this work, we explore a ternary anion system including the cyanide anion, expanding site disorder and introducing dipolar orientations as an additional degree of freedom. We prepared the series Li 6 PS 5 (CN) 1–x Br x , in which the dipolar cyanide anions are systematically diluted with bromide. We find that anion disorder, as determined by synchrotron and neutron diffraction and quantified by configurational entropy (S config ), is correlated with lowered activation barriers and increased lithium ion conductivity. We propose that S config describes the electrostatic heterogeneity of the Li environments, flattening the energetic landscape for ion transport. While anion substitution strongly impacts the activation barrier for transport, the temperature-independent Arrhenius prefactor does not follow the same trend. Through heat-capacity measurements of attempt frequency and deconvolution of terms within the prefactor, we rationalize the apparent decoupling of activation energy and prefactor to strong cyanide-lithium interactions that increase the entropy of migration. Together, these results expand the structure–property relationships in the argyrodite family to encompass multiple facets of disorder and the subsequent impact on lithium ion transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Unexpected crystalline homogeneity from the disordered bond network in La(Cr 0.2 Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 )O 3 films

Designing and understanding functional electronic and magnetic properties in perovskite oxides requires controlling and tuning the underlying crystal lattice. In this work, we report the structure, including oxygen and cation positions, of a single-crystal, entropy stabilized perovskite oxide film of La(Cr 0.2 Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 )O 3 grown on SrTiO3 (001). The parent materials range from orthorhombic (LaCrO 3 , LaMnO 3 , and LaFeO 3 ) to rhombohedral (LaCoO 3 and LaNiO 3 ), and first principles calculations indicate that these structural motifs are nearly degenerate in energy and should be highly distorted site to site. Despite this extraordinary local configurational disorder on the B-site sublattice, we find a structure with unexpected macroscopic crystalline homogeneity with a clear orthorhombic unit cell, whose orientation is demonstrated to be controlled by the strain and crystal structure of the substrate for films grown on (La 0.3 Sr 0.7 )(Al 0.65 Ta 0.35 )O 3 and NdGaO 3 (110). Furthermore, quantification of the atom positions within the unit cell reveals that the orthorhombic distortions are small, close to LaCrO 3, which may be driven by a combination of disorder averaging and the average ionic radii. This is a step toward understanding the rules for designing crystal motifs and tuning functional properties through controlled configurational complexity.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Towards accurate prediction of configurational disorder properties in materials using graph neural networks

Abstract The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient and accurate evaluations of configurational energies. Previous cluster expansion methods are not applicable to configurationally-complex material systems, including those with atomic distortions and long-range orders. In this work, we propose to leverage the versatile expressive capabilities of graph neural networks (GNNs) for efficient evaluations of configurational energies and present a workflow combining attention-based GNNs and Monte Carlo simulations to calculate the disorder properties. Using the dataset of face-centered tetragonal gold copper without and with local atomic distortions as an example, we demonstrate that the proposed data-driven framework enables the prediction of phase transition temperatures close to experimental values. We also elucidate that the variance of the energy deviations among configurations controls the prediction accuracy of disorder properties and can be used as the target loss function when training and selecting the GNN models. The work serves as a fundamental step toward a data-driven paradigm for the accelerated design of configurationally-complex functional material systems.

Chemistry↗
DOE OSTI2022

SPANet: Generalized permutationless set assignment for particle physics using symmetry preserving attention

The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19% - 35% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are available at https://github.com/Alexanders101/SPANet

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Machine-Learned Manifold-Based Models for Large Eddy Simulation of Turbulent Combustion

Reduced-order manifold combustion models are commonly used to lower the cost of reacting Large Eddy Simulations (LES) and may be derived either from physical principles as in flamelet models or from data-driven methods like Principal Component Analysis (PCA). In either case, neural networks are increasingly used as part of these models to provide a nonlinear mapping between a small set of pre-defined variables that parameterize the manifold and outputs of interest, such as reaction rates. In this work, we propose a new manifold-based modeling approach that combines the definition of the manifold-parameterizing variables (linear combinations of species), the nonlinear mapping to the outputs, and closure of filtered quantities for LES into the structure of a single neural network. This allows the process used to train the neural network to simultaneously optimize both the functional form of the model and the identities of the inputs to the model. The new approach can flexibly incorporate thermochemical data from any combustion system; if trained on data from 1D flames it can be interpreted as an optimized flamelet model, but it can also be used to learn models from data from more complex configurations. This work presents a priori evaluations of the new approach in both contexts. Evaluation using data from 1D premixed flames demonstrates the physical interpretability of the manifold variables generated by the new approach. Evaluation using data from direct numerical simulations of turbulent flames shows improved predictions relative to either flamelet or PCA-based models in a more complex configuration.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

An adaptive knowledge-based data-driven approach for turbulence modeling using ensemble learning technique under complex flow configuration: 3D PWR sub-channel with DNS data

This work describes a new approach to increase the accuracy of Reynolds-averaged Navier–Stokes (RANS) in modeling turbulence flow leveraging the machine learning technique. Traditionally, different turbulence models for Reynolds stress are developed for different flow patterns based on human knowledge. Each turbulence model has a certain application domain and prediction uncertainty. In recent years, with the rapid improvements of machine learning techniques, researchers start to develop an approach to compensate for the prediction discrepancy of traditional turbulence models with statistical models and data. However, the approach has deficiencies in several aspects. For example, the amount of human knowledge introduced to the statistical model couldn’t be controlled, which makes the statistical model learn from a very naïve stage and limits its application. In this work, a new approach is developed to address those deficiencies. Here, the new approach uses the “ensemble learning” technique to control the amount of human knowledge introduced into the statistical model. Therefore, the new approach could be adaptive to the multiple application domains. In conclusion, according to the results of case study, the new approach shows higher accuracy than both traditional turbulence models and the previous machine learning approach.

42 ENGINEERING↗
DOE OSTI2026

Towards Generalizable and Efficient Circuit Topology Design: A Graph-Transformer-based Surrogate Model with Curriculum Learning

Unlike circuit parameter and sizing optimizations, the automated design of analog circuit topologies poses significant challenges for learning-based approaches. One challenge arises from the combinatorial growth of the topology space with circuit size, which limits the topology optimization efficiency. Moreover, traditional circuit evaluation methods are time-consuming, while the presence of data discontinuity in the topology space makes the accurate prediction of circuit performance exceptionally difficult for unseen topologies. To tackle these challenges, we design a novel Graph-Transformer-based Network (GTN) as the surrogate model for circuit evaluation, offering a substantial acceleration in the speed of circuit topology optimization without sacrificing performance. Our GTN model architecture is designed to embed voltage changes in circuit loops and current flows in connected devices, enabling accurate performance predictions for circuits with unseen topologies. To address the cold start problem when scaling GTN to large-scale circuits, we further introduce a curriculum learning strategy that progressively trains GTN from small-scale to large-scale circuits. This approach enables the model to first learn fundamental physical principles from simpler topologies and gradually adapt to complex configurations, effectively bridging the circuit complexity gap and improving prediction accuracy. Taking the power converter circuit design as an experimental task, our GTN model significantly outperforms an analytical approach and baseline methods directly utilizing graph neural networks. Furthermore, GTN achieves less than 5% relative error and 196× speed-up compared with high-fidelity simulation. Notably, our GTN surrogate model empowers an automatic circuit design framework to discover circuits of comparable quality to those identified through high-fidelity simulation while reducing the time required by up to 98.2%. With curriculum learning, the enhanced GTN achieves a 51% improvement for performance prediction of large-scale circuits compared to the GTN model without this strategy. These advancements establish GTN as a scalable framework for automated analog circuit design across varying circuit complexity levels.

Lu, Haoshu [New Jersey Institute of Technology (NJ↗
DOE OSTI2021

Slip-free multiplication and complexity of dislocation networks in FCC metals

Abstract During plastic deformation of crystalline solids, intricate networks of dislocation lines form and evolve. To capture dislocation density evolution, prominent theories of crystal plasticity assume that 1) multiplication is driven by slip in active slip systems and 2) pair-wise slip system interactions dominate network evolution. In this work, we analyze a massive database of over 100 discrete dislocation dynamics simulations (with cross-slip suppressed), and our findings bring both of these assumptions into question. We demonstrate that dislocation multiplication is commonly observed on slip systems with no applied stress and no plastic strain rate, a phenomenon we refer to as slip-free multiplication. We show that while the formation of glissile junctions provides one mechanism for slip-free multiplication, additional mechanisms which account for the influence of coplanar interactions are needed to fully explain the observations. Unlike glissile junction formation which results from a binary reaction between a pair of slip systems, these new multiplication mechanisms require higher order reactions that lead to complex network configurations. While these complex configurations have not been given much attention previously, they account for about 50% of the line intersections in our database.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The HypA and HypB metallochaperones from Methanococcus maripaludis have unique metal-binding properties and a distinct nickel transfer mechanism

[NiFe] hydrogenases are widespread microbial metalloenzymes that catalyze the reversible conversion of hydrogen (H2) to protons and electrons, playing key roles in energy metabolism. The biosynthesis of the NiFe(CN) 2 CO cofactor involves a suite of maturation proteins, including the HypA and HypB nickel metallochaperones. Here, we define the metal-binding properties, nucleotide-dependent behavior, and functional interplay of HypA and HypB from the hydrogenotrophic methanogenic archaeon, Methanococcus maripaludis . Methanogens have multiple essential nickel-dependent enzymes, so they require efficient systems for nickel delivery that remain largely unexplored. Purified M. maripaludis HypA binds zinc or mononuclear iron at the C-terminal metal binding site, the latter of which has not been reported in other HypA proteins and may serve a unique regulatory role in methanogens. The G-protein metallochaperone HypB binds nickel at the G-domain, which stimulates GTPase activity. Size exclusion chromatography experiments reveal that HypA and HypB form complexes in the presence of nickel, and zinc-bound HypA is optimized for nickel transfer from HypB. The identity of the nucleotide bound to HypB (GDP or GTP) alters the oligomeric state of HypA-HypB complexes, supporting a GTPase-mediated nickel delivery pathway. The HypA-HypB 2 complex configuration is enriched and stable in the presence of GDP and nickel, indicating that this complex delivers nickel to the hydrogenase as opposed to HypA alone. Interestingly, affinity purification-mass spectrometry revealed that HypB interacts with several nickel-dependent proteins, suggesting that HypB may play a broader role in nickel homeostasis in M. maripaludis . Together, this work establishes a biochemical framework for HypAB-mediated nickel trafficking in methanogens.

[NiFe] hydrogenase↗
DOE OSTI2021

The Influence of Communication on the Complexity of Connected Lighting Systems

Experience in the Next Generation Lighting Systems (NGLS) ”Living Lab” at the Parsons School of Design, has clearly shown the impacts of communication on the configuration complexity of the connected lighting systems installed there. Since 2017, NGLS has invited manufacturers to submit connected lighting systems for assessment against a set of specifications. To date, 14 systems of wirelessly connected LED luminaires and controls have been installed in classrooms with existing lighting. In nine of the rooms, new LED linear pendants or recessed troffers replaced existing luminaires; in the other rooms, LED “kits” were retrofitted into the existing troffers. Eleven of the installations featured sensors integrated in the luminaires or retrofit kits; the others used ceiling mounted sensors. All systems provided wall control devices for manual operation. For half of the installations, the complete systems were furnished by single companies; for the other half, luminaires and controls were furnished by separate companies. A total of 18 different companies provided products, with four of these located outside the United States including, Korea, Poland, Germany, and the Netherlands. In addition, two different electrical contractors in New York City provided multiple installation teams totaling 10 individuals, none of whom were familiar with any of the systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Hole doping in compositionally complex correlated oxide enables tunable exchange biasing

Magnetic interfaces and the phenomena arising from them drive both the design of modern spintronics and fundamental research. Recently, it was revealed that through designing magnetic frustration in configurationally complex entropy stabilized oxides, exchange bias can occur in structurally single crystal films. This eliminates the need for complex heterostructures and nanocomposites in the design and control of magnetic response phenomena. In this work, we demonstrate through hole doping of a high entropy perovskite oxide that tuning of magnetic responses can be achieved. With detailed magnetometry, we show magnetic coupling exhibiting a variety of magnetic responses including exchange bias and antiferromagnetic spin reversal in the entropy stabilized ABO 3 perovskite oxide La 1-x Sr x (Cr 0.2 Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 )O 3 family. We find that manipulation of the A-site charge state can be used to balance magnetic phase compositions and coupling responses. This allows for the creation of highly tunable exchange bias responses. In the low Sr doping regime, a spin frustrated region arising at the antiferromagnetic phase boundary is shown to directly couple to the antiferromagnetic moments of the film and emerges as the dominant mechanism, leading to a vertical shift of magnetization loops in response to field biasing. At higher concentrations, direct coupling of antiferromagnetic and ferromagnetic regions is observed. This tunability of magnetic coupling is discussed within the context of these three competing magnetic phases, revealing critical features in designing exchange bias through exploiting spin frustration and disorder in high entropy oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nature of polariton transport in a Fabry-Perot cavity

Fabry-Perot microcavities can strongly enhance interactions between light and molecules, leading to the formation of hybrid light-matter states known as polaritons. Polaritons possess much smaller effective masses and much larger group velocities when the molecules are resonant with cavity modes that have finite (nonzero) in-plane wave vectors, giving rise to the possibilities of long-range and ultrafast ballistic transport. In this paper, we present the results of numerical simulations of the ultrafast ballistic transport phenomenon in real space and time during and after initialization with a short, spatially localized pulse. Here, we address this problem with two approaches: the standard transfer-matrix method (TMM) for planar structures and a second simulation based on the numerical solution of the Maxwell-Bloch equations that can be used for general configurations (complex metasurfaces, for instance) and excitation modes. The agreement between the TMM and the full numerical calculation with the Maxwell-Bloch equations when applied to multilayer planar configurations provides proof of the validity of both approaches for the present analyses. Overall, we find that the transport of the molecular excitons inside the cavity synchronize with the evolution of the enhanced electromagnetic field inside the cavity. Moreover, the synchronized transport rate is in good agreement with the group velocities predicted from a calculated dispersion relation across a wide range of frequencies. Finally, we relate the group velocity to the Hopfield coefficient obtained from quantum modeling and suggest that the dependence of light-matter coupling on the in-plane wave vector can be an important but overlooked factor for understanding the transport behavior of polaritons. These simulations provide an intuitive tool for understanding the collective motion of light and excitons and helps us to better understand how experimental observations of polaritons should be interpreted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Tomographic imaging of atmospheric pressure plasma on complex surfaces

Many plasma types and behaviors such as streamer, arcs, cathode spots, anode spots, ionization waves, and magnetic field interactions create non-symmetric, fully 3D plasma structures. The plasma distribution in 3D space is heavily influenced by complex surfaces and the coupling interactions between plasma properties and the interfacing material properties. For example, ionization waves propagate in directions where ionization rates are highest, leading to complex configurations that are not fully understood or well characterized. Recent advances in laser diagnostics and models have been able to investigate well-controlled idealized plasmas in 2D fashion, but the complex structure in actual plasmas requires a technique than can provide a more complete 3D picture. However, 3D plasma diagnostics do not currently exist. To address this limitation, this activity will leverage available equipment to build a new tomographic optical imaging capability and advance the state-of-the-art in plasma diagnostics to investigate 3D phenomena on complex surfaces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Document Management: A Focused Analysis on the Configuration Management Process at Y-12 National Security Complex

The configuration management process at Y-12 is a crucial process to ensure safe operations and compliance with Department of Energy (DOE) regulations and requirements. This process is evaluated utilizing systems-thinking methodology in order to understand the current operational flow, with the ultimate goal of building an easily understandable, representative model. This model offers insights on key areas of waste and provides a potential solution by modernizing rate limiting steps: a software implementation to update the traditional document management system and make it paperless. Stakeholders of the software implementation are identified in order to define characteristic requirements of an effective product and the anticipated benefit is evaluated. Ultimately, this project is an effort to increase the quality of life for all stakeholders, while improving the capacity, throughput, and effectiveness of a necessary process at Y-12.

42 ENGINEERING↗
DOE OSTI2022

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

Abstract Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of atomic configurations. To address this challenge, we present Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that accounts for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt 3 Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combined with low symmetry of the alloy substrate produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, where configurational complexity results from the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions. In both cases, the ACE-GCN model, trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully describes trends in the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.

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

Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling

Abstract Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond that accessible by ab initio methods and play an increasingly important role in the study and design of materials. However, MLIPs are only as accurate and robust as the data on which they are trained. Here, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolates more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with the M3GNet universal potential can be used instead of expensive ab initio MD to rapidly create a large configuration space for target systems. We combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures. This work paves the way for robust high-throughput development of MLIPs across any compositional complexity.

Qi, Ji (ORCID:0000000158089931)↗