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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example

Deriving closed-form analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM—the Desai-Zwanzig model—in its mean-field limit, using a smaller number of variables than traditional closed-form models. To this end, we use the manifold learning algorithm Diffusion Maps to identify a parsimonious set of data-driven latent variables, and we show that they are in one-to-one correspondence with the expected theoretical order parameter of the ABM. We then utilize a deep learning framework to obtain a conformal reparametrization of the data-driven coordinates that facilitates, in our example, the identification of a single parameter-dependent ordinary differential equation (ODE) in these coordinates. Additionally, we identify this ODE through a residual neural network inspired by a numerical integration scheme (forward Euler). We then use the identified ODE—enabled through an odd symmetry transformation—to construct the bifurcation diagram exhibiting the phase transition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Asymmetric Fraunhofer spectra in a topological insulator-based Josephson junction

Josephson junctions with topological insulators as their weak link (S–TI–S junctions) are predicted to host Majorana fermions, which are key to creating qubits for topologically protected quantum computing. But the details of the S–TI–S current-phase relation and its interplay with magnetic fields are not well understood. We fabricate a Bi 2 Se 3 junction with NbTi leads and measure the Fraunhofer patterns of the junction with applied in-plane fields. We observe that asymmetric Fraunhofer patterns appear in the resistance maps of B z vs B x,y , with aperiodic node spacings. These asymmetric patterns appear even at zero parallel field and for temperatures up to 1 K. The anomalous features are compared to asymmetric Fraunhofer patterns expected for finite Cooper pair momentum shifts as well as geometric effects. We show that the geometric effects can dominate, independent of in-plane field magnitude. Furthermore, these results are important for differentiating geometrical phase shifts from those caused by Cooper pair momentum shifting, Majorana mode signatures, or other unconventional superconducting behavior.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO 2 sequestration accelerated with machine learning

Successful geologic CO 2 storage projects depend on numerical simulations to predict reservoir performance during site selection, injection verification, and post-injection monitoring phases of the project. These numerical simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence, individual simulations often require tens to hundreds of hours to complete on high-performance computing clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative permeability exhibit substantial variability, even within the same rock type. This combination of computational expense and wide-ranging parametric variability means that there remains substantial uncertainty in the behavior of multi-phase CO 2 -water systems, particularly in the context of feedbacks between relative permeability and capillary pressure. To bridge this knowledge gap, here we develop a novel workflow that utilizes physics-based numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate parameter space that governs both capillary pressure and relative permeability. With this approach, the ANN is trained to emulate both fluid pressure distribution and CO 2 saturation, which are then interrogated quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this study initially show that capillary entry pressure is the dominant control on both CO 2 plume geometry and fluid pressure propagation when considering the combined effects of capillary pressure and relative permeability, particularly when phase interference is low and residual CO 2 saturation is high. Moreover, the ANN emulator provides tremendous computational speed-up by computing 2691 individual simulations in several minutes; whereas, the same simulation ensemble would have required ~3 years of simulation time using only physics-based simulation methods (25,000 times speed up).

58 GEOSCIENCES↗
DOE OSTI2023

Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC

Abstract Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both ab initio calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Probing the Kitaev honeycomb model on a neutral-atom quantum computer

Quantum simulations of many-body systems are among the most promising applications of quantum computers. In particular, models based on strongly correlated fermions are central to our understanding of quantum chemistry and materials problems, and can lead to exotic, topological phases of matter. However, owing to the non-local nature of fermions, such models are challenging to simulate with qubit devices. Here we realize a digital quantum simulation architecture for two-dimensional fermionic systems based on reconfigurable atom arrays. We utilize a fermion-to-qubit mapping based on Kitaev’s model on a honeycomb lattice, in which fermionic statistics are encoded using long-range entangled states. We prepare these states efficiently using measurement and feedforward, realize subsequent fermionic evolution through Floquet engineering with tunable entangling gates interspersed with atom rearrangement, and improve results with built-in error detection. Leveraging this fermion description of the Kitaev spin model, we efficiently prepare topological states across its complex phase diagram and verify the non-Abelian spin-liquid phase by evaluating an odd Chern number. We further explore this two-dimensional fermion system by realizing tunable dynamics and directly probing fermion exchange statistics. Finally, we simulate strong interactions and study the dynamics of the Fermi–Hubbard model on a square lattice. These results pave the way for digital quantum simulations of complex fermionic systems for materials science, chemistry and high-energy physics.

atomic and molecular physics↗
DOE OSTI2022

Visualizing Electric and Magnetic Field Coupling in Au-Nanorod Trimer Structures via Stimulated Electron Energy Gain and Cathodoluminescence Spectroscopy: Implications for Meta-Atom Imaging

In this work, trimer meta-atoms composed of three gold rods in an equilateral triangular geometry were fabricated, and their near-field plasmonic responses were characterized via electron energy loss (EEL), cathodoluminescence (CL), and stimulated electron energy loss/gain (sEEL/sEEG) spectroscopy. The trimer structure hybridizes into a low-energy mode with all three rods coupling in-phase, which produces a circulating current and thus a magnetic field. The next highest-energy mode consists of two rods coupling out-of-phase and produces a net electric dipole. We investigate the near fields of hybridized magnetic and electric modes via EEL and CL and correlate their spectral characteristics and intensity maps. Then, by changing the length of the trimer rods, we tune the magnetic and electric modes to our laser energy and characterize the excited state via sEEL/sEEG spectroscopy. Exploration of the tilt dependence, relative to the optical source, of the two modes reveals that the electric mode sEEG intensity increases more than the expected sin 2 (θ) dependence of the optical electric field coupling (see the Supporting Information for a detailed description). After correcting for the tail of the close-proximity electric mode, we demonstrate sEEG via coupling of the magnetic component of the optical field to the magnetic meta-atoms, which has the expected cos 2 (θ) tilt dependence. This realization opens the possibility to explore the nanoscale excited-state near-field imaging of other magnetic meta-atom structures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H 2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

42 ENGINEERING↗
DOE OSTI2020

Symmetric Bloch oscillations of matter waves

Cold atoms in an optical lattice provide an ideal platform for studying Bloch oscillations. Here we extend Bloch oscillations to two superposed optical lattices that are accelerated away from one another, and show that these symmetric Bloch oscillations can split, reflect, and recombine matter waves coherently. Using the momentum parity symmetry of the Hamiltonian, we map out the energy band structure of the process and show that superpositions of momentum states are created by adiabatically following the ground state of the Hamiltonian. The relative phase and velocity of the two lattices completely determines the trajectories of different branches of the matter wave. Experimentally we demonstrate symmetric Bloch oscillations using cold cesium atoms where we form interferometers with up to 240ℏk momentum splitting, one of the largest coherent momentum splittings achieved to date. This work has applications in macroscopic tests of quantum mechanics, measurements of fundamental constants, and searches for new physics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Map of the Zintl AM 2 Pn 2 Compounds: Influence of Chemistry on Stability and Electronic Structure

The AM 2 Pn 2 (A= Ca, Sr, Ba, Yb, Mg; M = Zn, Cd, Mg; and Pn = N, P, As, Sb, Bi) family of Zintl phases has been known as thermoelectric materials and has recently gained much attention for highly promising materials for solar absorbers in single-junction and tandem solar cells. In this paper, we will, from first principles, explore the entire family of AM 2 Pn 2 compounds in terms of their ground-state structure, thermodynamic stability, and electronic structure. We also perform photoluminescence spectroscopy on bulk powder and thin film samples to verify our results, including the first measurements of the band gaps of SrCd 2 P 2 and CaCd 2 P 2 . The AM 2 Pn 2 compounds exhibit broad stability, are mostly isostructural to CaAl 2 Si 2 (P$\overline{3}$m1), and cover a wide range of band gaps from 0 to beyond 3 eV. This could make them useful for a variety of purposes, for which we propose several candidates, such as CaZn 2 N 2 for tandem top cell solar absorbers and SrCd 2 Sb 2 and CaZn 2 Sb 2 for infrared detectors. By examining the band structures of the AM 2 Pn 2 , we find that Mg 3 Sb 2 has the most promise as a thermoelectric material due to several off-Γ valence band pockets, which are unique to it among the compositions studied here.

14 SOLAR ENERGY↗
DOE OSTI2021

Scanning SQUID characterization of extremely overdoped La 2-x Sr x CuO 4

Recently, advances in film synthesis methods have enabled a study of extremely overdoped La 2 - x Sr x Cu O 4 . This has revealed a surprising behavior of the superfluid density as a function of doping and temperature, the explanation of which is vividly debated. One popular class of models posits electronic phase separation, where the superconducting phase fraction decreases with doping, while some competing phase (e.g., ferromagnetic) progressively takes over. A problem with this scenario is that all the way up to the dome edge the superconducting transition remains sharp, according to mutual inductance measurements. However, the physically relevant scale is the Pearl penetration depth Λ P , and this technique probes the sample on a length scale L that is much larger than Λ P . In the present paper, we use local scanning superconducting quantum interference device (SQUID) measurements that probe the susceptibility of the sample on the scale L << Λ P . Our SQUID maps show uniform landscapes of susceptibility and excellent overall agreement of the local penetration depth data with the bulk measurements. Overall, these results contribute an important piece to the puzzle of how high-temperature superconductivity vanishes on the overdoped side of the cuprate phase diagram.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Microstructural Evaluation of Phase Instability in Large Bandgap Metal Halide Perovskites

The optoelectronic performance of organic–inorganic halide perovskite (OIHP)-based devices has been improved in recent years. Particularly, solar cells fabricated using mixed-cations and mixed-halides have outperformed their single-cation and single-halide counterparts. Yet, a systematic evaluation of the microstructural behavior of mixed perovskites is missing despite their known composition-dependent photoinstability. Here, we explore microstructural inhomogeneity in (FAPbI 3 ) x (MAPbBr 3 ) 1– x using advanced scanning probe microscopy techniques. In this work, contact potential difference (CPD) maps measured by Kelvin probe force microscopy show an increased fraction of grains exhibiting a low CPD with flat topography as MAPbBr 3 concentration is increased. The higher portion of low CPD contributes to asymmetric CPD distribution curves. Chemical analysis reveals these grains being rich in MA, Pb, and I. The composition-dependent phase segregation upon illumination, reflected on the emergence of a low-energy peak emission in the original photoluminescence spectra, arises from the formation of such grains with flat topology. Bias-dependent piezo-response force microscopy measurements, in these grains, further confirm vigorous ion migration and cause a hysteretic piezo-response. Our results, therefore, provide insights into the microstructural evaluation of phase segregation and ion migration in OIHPs pointing toward process optimization as a mean to further enhance their optoelectronic performance.

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

Laser Powder Bed Fusion of ODS 14YWT from Gas Atomization Reaction Synthesis Precursor Powders

Abstract Laser powder bed fusion (LPBF) additive manufacturing (AM) is a promising route for the fabrication of oxide dispersion strengthened (ODS) steels. In this study, 14YWT ferritic steel powders were produced by gas atomization reaction synthesis (GARS). The rapid solidification resulted in the formation of stable, Y-containing intermetallic Y 2 Fe 17 on the interior of the powder and a stable Cr-rich oxide surface. The GARS powders were consolidated with LPBF. Process parameter maps identified a stable process window resulting in a relative density of 99.8%. Transmission electron microscopy and high-energy x-ray diffraction demonstrated that during LPBF, the stable phases in the powder dissociated in the liquid melt pool and reacted to form a high density (1.7 × 10 20 /m 3 ) of homogeneously distributed Ti 2 Y 2 O 7 pyrochlore dispersoids ranging from 17 to 57 nm. The use of GARS powder bypasses the mechanical alloying step typically required to produce ODS feedstock. Preliminary mechanical tests demonstrated an ultimate tensile and yield strength of 474 MPa and 312 MPa, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Extremized nonlinear and linearized responses in soft metamaterials enabled by gradient-based design and grayscale digital light processing

In this study, we develop a gradient-based design approach that exploits grayscale digital light processing (DLP) 3D printing for extremizing the nonlinear and linearized response of soft metamaterials — materials that harness engineered geometric instabilities to undergo large and programmable changes in configuration. Grayscale DLP approaches modulate local mechanical properties at the pixel scale by tuning the light intensity within a single grayscale image, unlocking an exceptionally large design space. To effectively navigate this space, we develop smooth mappings between local light intensity values and global quantities of interest that characterize the behavior of soft metamaterials. Enabling these smooth mappings are robust and differentiable nonlinear finite element simulations powered by a trust region solver. A PDE-constrained optimization problem is then solved to invert these mappings and produce light intensity distributions that endow the printed part with varying stiffness and flexibility in distinctive regions. It is shown that optimizing the distribution of soft and stiff phases throughout a metamaterial structure results in markedly different buckling and self-contact configurations to drive extremized nonlinear compression and linearized vibration responses. Optimized light intensity distributions are translated to grayscale images and directly used to print soft metamaterial samples, showing remarkable agreement between the buckling and self-contact response in simulated and measured deformed configurations.

Additive manufacturing↗
DOE OSTI2024

CFD unified approach under Eulerian–Lagrangian framework for methanol and gasoline direct injection sprays in evaporative and flash boiling conditions

Innovative synthetic fuels for advanced propulsion systems, such as methanol and ammonia, and synthetic blended fuels (E00, E10, and E30), known for their high volatility, are often injected directly into combustion chambers. It follows that Eulerian–Lagrangian spray models need to accurately capture the spray collapse as a consequence of flash boiling onset and be capable of proficiently handling the preferential evaporation of multi-component fuels in evaporative scenarios. So, we performed the assessment of an Eulerian–Lagrangian CFD code for simulating methanol and E00 gasoline blend sprays in both early and late injection conditions involving flash boiling conditions and preferential evaporation. The adoption of an effervescent breakup model and of a non-equilibrium phase transition model for the discrete phase allows the adoption of a setup that is almost completely free from specific constant tuning, especially for what concerns the breakup model. We validated the simulations using experimental PLV maps of methanol and E00 sprays issued from the ECN Spray M injector. The results highlight a significantly different morphology of the methanol spray compared to the E00 one under late injection conditions. Under stratified combustion, low-volatile fuels are likely to be ignited first, and the flame propagates toward the high-volatile fuels. In conclusion, the spray collapse was also correctly reproduced, inducing the presence of a low-pressure zone and modifying the spray morphology.

E00↗
DOE OSTI2023

Coupled Lattice Boltzmann Modeling Framework for Pore-Scale Fluid Flow and Reactive Transport

In this paper, we propose a modeling framework for pore-scale fluid flow and reactive transport based on a coupled lattice Boltzmann model (LBM). We develop a modeling interface to integrate the LBM modeling code parallel lattice Boltzmann solver and the PHREEQC reaction solver using multiple flow and reaction cell mapping schemes. The major advantage of the proposed workflow is the high modeling flexibility obtained by coupling the geochemical model with the LBM fluid flow model. Consequently, the model is capable of executing one or more complex reactions within desired cells while preserving the high data communication efficiency between the two codes. Meanwhile, the developed mapping mechanism enables the flow, diffusion, and reactions in complex pore-scale geometries. We validate the coupled code in a series of benchmark numerical experiments, including 2D single-phase Poiseuille flow and diffusion, 2D reactive transport with calcite dissolution, as well as surface complexation reactions. The simulation results show good agreement with analytical solutions, experimental data, and multiple other simulation codes. In addition, we design an AI-based optimization workflow and implement it on the surface complexation model to enable increased capacity of the coupled modeling framework. Compared to the manual tuning results proposed in the literature, our workflow demonstrates fast and reliable model optimization results without incorporating pre-existing domain knowledge.

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

Assessing Uncertainties and Approximations in Solar Heating of the Climate System

In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate-relevant scales. We build benchmarking capability around a solar heating code (Solar-J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice-cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m -2 using a full climate re-creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ~2 W m -2 with greater latitudinal biases and are likely to affect a model’s ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.

58 GEOSCIENCES↗
DOE OSTI2020

SPIDERS: overview of the X-ray galaxy cluster follow-up and the final spectroscopic data release

ABSTRACT SPIDERS (The SPectroscopic IDentification of eROSITA Sources) is a large spectroscopic programme for X-ray selected galaxy clusters as part of the Sloan Digital Sky Survey-IV (SDSS-IV). We describe the final data set in the context of SDSS Data Release 16 (DR16): the survey overall characteristics, final targeting strategies, achieved completeness, and spectral quality, with special emphasis on its use as a galaxy cluster sample for cosmology applications. SPIDERS now consists of about 27 000 new optical spectra of galaxies selected within 4000 photometric red sequences, each associated with an X-ray source. The excellent spectrograph efficiency and a robust analysis pipeline yield a spectroscopic redshift measurement success rate exceeding 98 per cent, with a median velocity accuracy of 20 km s−1 (at z = 0.2). Using the catalogue of 2740 X-ray galaxy clusters confirmed with DR16 spectroscopy, we reveal the 3D map of the galaxy cluster distribution in the observable Universe up to z ∼ 0.6. We highlight the homogeneity of the member galaxy spectra among distinct regions of the galaxy cluster phase space. Aided by accurate spectroscopic redshifts and by a model of the sample selection effects, we compute the galaxy cluster X-ray luminosity function and we present its lack of evolution up to z = 0.6. Finally we discuss the prospects of forthcoming large multiplexed spectroscopic programmes dedicated to follow up the next generation of all-sky X-ray source catalogues.

Clerc, N.↗