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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

First-Principles Studies on Sc 2 RuZ (Z = Si, Ge, Sn) Inverse Heusler Alloys: Structural, Electronic, and Transport Properties

The continuous demand for efficient, nontoxic, and thermally stable materials for room-temperature energy conversion motivates the exploration of novel thermoelectric systems beyond the traditional magnetic Heusler alloys. While full and half-Heusler compounds, especially Co-, Ni-, and Mn-based systems, have demonstrated promising thermoelectric properties, their typically high operating temperatures and magnetic complexities limit their applicability in ambient thermal management. In this context, we investigate whether Sc-based inverse Heusler alloys can offer a viable nonmagnetic alternative with competitive thermoelectric performance. In this work, we perform a systematic first-principles study of the inverse Heusler compounds Sc 2 RuZ (Z = Si, Ge, Sn), focusing on their structural, electronic, mechanical, and thermodynamic-thermoelectric properties. Density Functional Theory (DFT) was employed to compute optimized lattice structures and band dispersion, while dynamical stability was assessed via phonon calculations. Thermoelectric transport coefficients, including Seebeck coefficient, electrical conductivity, and thermal conductivity, were estimated using the semiclassical Boltzmann transport theory within the constant relaxation time approximation. Our results show that all Sc 2 RuZ compounds are thermodynamically stable semiconductors with indirect band gaps of 0.12–0.16 eV and exhibit high elastic moduli, especially Sc 2 RuSn, which demonstrates superior stiffness and incompressibility. Importantly, all compounds display promising room-temperature thermoelectric characteristics, including high Seebeck coefficients and power factors. These findings reveal that Sc 2 RuZ alloys represent a rare class of stable, nonmagnetic inverse Heusler semiconductors with intrinsic thermoelectric potential at room temperature, unlike many existing Heusler systems optimized for spintronics or high-temperature operation. This work expands the known design space for Heusler-based thermoelectrics and offers a theoretical basis for experimental realization of efficient, low-temperature, nonmagnetic thermoelectric materials.

alloys↗
DOE OSTI2023

Multi-Electron Transfer at H-Terminated p-Si Electrolyte Interfaces: Large Photovoltages under Inversion Conditions

Here, photovoltages for hydrogen terminated p-Si(111) in an acetonitrile electrolyte were quantified with methyl viologen [1,1'-(CH 3 ) 2 -4,4'-bipyridinium](PF 6 ) 2 , abbreviated MV 2+ , and [Ru(bpy) 3 ](PF 6 ) 2 , where bpy is 2,2'-bipyridine, that respectively undergo two and three one-electron transfer reductions. The reduction potentials, E°, of the two MV 2+ reductions occurred at energies within the forbidden bandgap, while the three [Ru(bpy) 3 ] 2+ reductions occurred within the continuum of conduction band states. Bandgap illumination resulted in reduction that were more positive than that measured with a degenerately doped n + -Si demonstrative of a photovoltage, Vph, that increased in the order: MV 2+/+ (260 mV) < MV +/0 (400 mV) < Ru 2+/+ (530 mV) ~ Ru +/0 (540 mV) ~ Ru 0/– (550 mV). Pulsed 532 nm excitation generated electron-hole pairs whose dynamics were nearly constant under depletion conditions and increased markedly as the potential was raised or lowered. A long wavelength absorption feature assigned to conduction band electrons provided additional evidence for the presence of an inversion layer. Collectively, the data reveal that the most optimal photovoltage, as well at the longest electron-hole pair lifetime and the highest surface electron concentration, occur when E° lies energetically within the unfilled conduction band states where an inversion layer is present. The bell-shaped dependence for electron-hole pair recombination with the surface potential was predicted by the time-honored SRH model providing a clear indication that this interface provides access to all four bias conditions, i.e., accumulation, flat band, depletion, and inversion. The implications of these findings for photocatalysis applications and solar energy conversion are discussed.

14 SOLAR ENERGY↗
DOE OSTI2022

Three‐Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography

Abstract Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three‐dimensional (3‐D) time‐lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection behavior, an encoder‐decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN can accurately capture the 3‐D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using synthetic test datasets and then test the accuracy on a suite of experimental PET imaging datasets acquired on four different geologic cores. The network‐predicted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging data. The results indicate that a single trained network can generate robust 3‐D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimentally observed solute arrival time behavior. This work provides an unprecedented approach for efficiently characterizing multiscale permeability heterogeneity in complex geologic samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multi‐Scale Seismic Imaging of the Ridgecrest, CA, Region With Waveform Inversion of Regional and Dense Array Data

Abstract We develop an inversion procedure for deriving multi‐scale velocity models with waveform inversions of earthquake and ambient noise data at multi‐frequency bands recorded by regional and dense sensor configurations. The method is applied for the area around the 2019 Ridgecrest earthquake rupture zones, utilizing data recorded by regional stations and dense 2D and 1D arrays with station spacings of ∼5 km and ∼100 m, respectively. Starting with regional Vp, Vs models and locations of Ridgecrest aftershocks, the velocity models and event locations are improved iteratively by inversions of waveforms recorded by regional stations and the 2D array, using a minimum spectral element size of ∼600 m. Waveforms from local events recorded by dense 1D arrays across the M7.1 rupture zone with frequencies of up to 10 Hz are used to resolve small‐scale features of the rupture zone and shallow crust with a local spectral element size of 80 m. The refined models provide self‐consistent descriptions of the rupture zone and the shallow crust embedded in the regional structures. The results reveal pronounced low Vs and high Vp/Vs in the M6.4 and M7.1 rupture zones coinciding with concentrations of seismicity, and also around the Garlock fault and in several local basins. We also observe clear velocity contrasts across the Garlock fault with polarity reversals along strike and with depth. The obtained multi‐scale velocity models can be used to improve derivations of earthquake source properties, simulations of dynamic ruptures and ground motions, and the understanding of fault and tectonic processes in the region.

Geochemistry & Geophysics↗
DOE OSTI2024

WUS324: Multiscale Full Waveform Inversion Approaching Convergence Improves Waveform Fits While Imaging Seismic Structure of the Western United States

Abstract We report a new model of radially anisotropic crustal and upper mantle structure of the western United States (WUS324) obtained from full waveform inversion of earthquake data. We ran three multiscale inversion stages beyond model WUS256 (Rodgers et al., 2022, https://doi.org/10.1029/2022jb024549 ) allowing them to approach convergence to fit a larger data set to a shorter minimum period of 16 s. WUS324 is based on 324 total iterations from its starting model, significantly more (16 times) than previous studies. Waveform misfit reductions are 66%–70% for both the inversion data and an independent validation data set providing confidence in the predictive power of the model. WUS324 provides much better fits and reveals shear wavespeed, v S , structure of this large region with more detail than previous waveform tomography models. We show representative images demonstrating the resolution of diverse seismic structure across this highly heterogeneous region including oceanic lithosphere, subducting slabs and continental magmatism.

58 GEOSCIENCES↗
DOE OSTI2020

Inverse ZrO 2 /Cu as a highly efficient methanol synthesis catalyst from CO 2 hydrogenation

Enhancing the intrinsic activity and space time yield of Cu based heterogeneous methanol synthesis catalysts through CO 2 hydrogenation is one of the major topics in CO 2 conversion into value-added liquid fuels and chemicals. Here we report inverse ZrO 2 /Cu catalysts with a tunable Zr/Cu ratio have been prepared via an oxalate co-precipitation method, showing excellent performance for CO 2 hydrogenation to methanol. Under optimal condition, the catalyst composed by 10% of ZrO 2 supported over 90% of Cu exhibits the highest mass-specific methanol formation rate of 524 g MeOH kg cat -1 h -1 at 220°C, 3.3 times higher than the activity of traditional Cu/ZrO 2 catalysts (159 g MeOH kg cat -1 h -1 ). In situ XRD-PDF, XAFS and AP-XPS structural studies reveal that the inverse ZrO 2 /Cu catalysts are composed of islands of partially reduced 1–2 nm amorphous ZrO 2 supported over metallic Cu particles. The ZrO 2 islands are highly active for the CO 2 activation. Meanwhile, an intermediate of formate adsorbed on the Cu at 1350 cm -1 is discovered by the in situ DRIFTS. This formate intermediate exhibits fast hydrogenation conversion to methoxy. The activation of CO 2 and hydrogenation of all the surface oxygenate intermediates are significantly accelerated over the inverse ZrO 2 /Cu configuration, accounting for the excellent methanol formation activity observed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Inverse mapping of properties to composition through generative modeling for designing molten salts

Generative modeling (GM) has been increasingly used for the inverse design and optimization of materials, yet its application to molten salt mixtures remains unexplored despite how a successful approach to the inverse design of molten salts would contribute to efficiently exploiting their customizability and unlocking their advantages in applications, such as energy production and energy storage. This work presents a workflow for the inverse design of molten salts with targeted density values, addressing the challenge of representing these complex mixtures in GM. A dataset of critically evaluated molten salt densities is used to train a variational autoencoder coupled with a predictive deep neural network, which then can be used to generate new molten salt compositions with desired density values. The effectiveness of the approach is demonstrated by designing mixtures with distinct densities and validating the predicted values using ab initio molecular dynamics simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Enhanced condensation heat transfer using porous silica inverse opal coatings on copper tubes

Abstract Phase-change condensation is commonplace in nature and industry. Since the 1930s, it is well understood that vapor condenses in filmwise mode on clean metallic surfaces whereas it condenses by forming discrete droplets on surfaces coated with a promoter material. In both filmwise and dropwise modes, the condensate is removed when gravity overcomes pinning forces. In this work, we show rapid condensate transport through cracks that formed due to material shrinkage when a copper tube is coated with silica inverse opal structures. Importantly, the high hydraulic conductivity of the cracks promote axial condensate transport that is beneficial for condensation heat transfer. In our experiments, the cracks improved the heat transfer coefficient from ≈ 12 kW/m 2 K for laminar filmwise condensation on smooth clean copper tubes to ≈ 80 kW/m 2 K for inverse opal coated copper tubes; nearly a sevenfold increase from filmwise condensation and identical enhancement with state-of-the-art dropwise condensation. Furthermore, our results show that impregnating the porous structure with oil further improves the heat transfer coefficient by an additional 30% to ≈ 103 kW/m 2 K. Importantly, compared to the fast-degrading dropwise condensation, the inverse opal coated copper tubes maintained high heat transfer rates when the experiments were repeated > 20 times; each experiment lasting 3–4 h. In addition to the new coating approach, the insights gained from this work present a strategy to minimize oil depletion during condensation from lubricated surfaces.

Adera, Solomon↗
DOE OSTI2020

Grand canonical inverse design of multicomponent colloidal crystals

Inverse design methods are powerful computational approaches for creating colloidal systems which self-assemble into a target morphology by reverse engineering the Hamiltonian of the system. Despite this, these optimization procedures tend to yield Hamiltonians which are too complex to be experimentally realized. An alternative route to complex structures involves the use of several different components, however, conventional inverse design methods do not explicitly account for the possibility of phase separation into compositionally distinct structures. We present an inverse design scheme for multicomponent colloidal systems by combining active learning with a method to directly compute their ground state phase diagrams. This explicitly accounts for phase separation and can locate stable regions of Hamiltonian parameter space which grid-based surveys are prone to miss. Using this we design low-density, binary structures with Lennard-Jones-like pairwise interactions that are simpler than in the single component case and potentially realizable in an experimental setting. This reinforces the concept that ground states of simple, multicomponent systems might be rich with previously unappreciated diversity, enabling the assembly of non-trivial structures with only few simple components instead of a single complex one.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning inversion from small-angle scattering for charged polymers

We develop Monte Carlo simulations for uniformly charged polymers and a machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, which can be obtained from small-angle scattering experiments. The polymer is modeled as a chain of fixed-length bonds, where the connected bonds are subject to bending energy, and there is also a screened Coulomb potential for charge interaction between all joints. The bending energy is determined by the intrinsic bending stiffness, and the charge interaction depends on the interaction strength and screening length. All three contribute to the stiffness of the polymer chain and lead to longer and larger polymer conformations. The screening length also introduces a second length scale for the polymer besides the bending persistence length. To obtain the inverse mapping from the structure factor to these polymer conformation and energy-related parameters, we generate a large data set of structure factors by running simulations for a wide range of polymer energy parameters. We use principal component analysis to investigate the intra-polymer structure factors and determine the feasibility of the inversion using the nearest neighbor distance. We employ Gaussian process regression to achieve the inverse mapping and extract the characteristic parameters of polymers from the structure factor with low relative error.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A domain decomposition preconditioning for an inverse volume scattering problem

In this work, we propose domain decomposition preconditioners for the solution of an integral equation formulation of the acoustic forward and inverse scattering problems. We study both forward and inverse volume problems and propose preconditioning techniques to accelerate the iterative solvers. For the forward scattering problem, we extend the domain decomposition based preconditioning techniques presented for partial differential equations in Cai and Sarkis (1999 SIAM J. Sci. Comput. 21 792–797), to integral equations. We combine this domain decomposition preconditioner with a low-rank correction, which is easy to construct, forming a new preconditioner. For the inverse scattering problem, we use the forward problem preconditioner as a building block for constructing a preconditioner for the Gauss–Newton Hessian. We present numerical results that demonstrate the performance of both preconditioning strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

An autoencoder compression approach for accelerating large-scale inverse problems

Abstract Partial differential equation (PDE)-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes required to accurately compute the PDE solution introduce an enormous number of parameters and require large-scale computing resources such as more processors and more memory to solve such systems in a reasonable time. For inverse problems constrained by time-dependent PDEs, the adjoint method often employed to compute gradients and higher order derivatives efficiently requires solving a time-reversed, so-called adjoint PDE that depends on the forward PDE solution at each timestep. This necessitates the storage of a high-dimensional forward solution vector at every timestep. Such a procedure quickly exhausts the available memory resources. Several approaches that trade additional computation for reduced memory footprint have been proposed to mitigate the memory bottleneck, including checkpointing and compression strategies. In this work, we propose a close-to-ideal scalable compression approach using autoencoders to eliminate the need for checkpointing and substantial memory storage, thereby reducing the time-to-solution and memory requirements. We compare our approach with checkpointing and an off-the-shelf compression approach on an earth-scale ill-posed seismic inverse problem. The results verify the expected close-to-ideal speedup for the gradient and Hessian-vector product using the proposed autoencoder compression approach. To highlight the usefulness of the proposed approach, we combine the autoencoder compression with the data-informed active subspace (DIAS) prior showing how the DIAS method can be affordably extended to large-scale problems without the need for checkpointing and large memory.

Mathematics↗
DOE OSTI2022

Quantum error mitigation by hidden inverses protocol in superconducting quantum devices

We present a method to improve the convergence of variational algorithms based on hidden inverses (HIs) to mitigate coherent errors. In the context of error mitigation, this means replacing the hardware implementation of certain Hermitian gates with their inverses. Doing so results in noise cancellation and a more resilient quantum circuit. This approach improves performance in a variety of two-qubit error models where the noise operator also inverts with the gate inversion. We apply the mitigation scheme on superconducting quantum processors running the variational quantum eigensolver (VQE) algorithm to find the H 2 ground-state energy. When implemented on superconducting hardware we find that the mitigation scheme effectively reduces the energy fluctuations in the parameter learning path in VQE, reducing the number of iterations for a converged value. We also provide a detailed numerical simulation of VQE performance under different noise models and explore how HIs and randomized compiling affect the underlying loss landscape of the learning problem. These simulations help explain our experimental hardware outcomes, helping to connect lower-level gate performance to application-specific behavior in contrast to metrics like fidelity which often do not provide an intuitive insight into observed high level performance.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Inverse problem in the large momentum effective theory framework

One proposal to compute parton distributions from first principles is the large momentum effective theory (LaMET), which requires the Fourier transform of matrix elements computed nonperturbatively. Lattice quantum chromodynamics (QCD) provides calculations of these matrix elements over a finite range of Fourier harmonics that are often noisy or unreliable in the largest computed harmonics. It has been suggested that enforcing an exponential decay of the missing harmonics helps alleviate this issue. Using nonperturbative data, we show that the uncertainty introduced by this inverse problem in a realistic setup remains significant without very restrictive assumptions, and that the importance of the exact asymptotic behavior is minimal for values of 𝑥 where the framework is currently applicable. We show that the crux of the inverse problem lies in harmonics of the order of 𝜆 = 𝑧⁢𝑃 𝑧 ∼ 5–15, where the signal in the lattice data is often barely existent in current studies, and the asymptotic behavior is not firmly established. We stress the need for more sophisticated techniques to account for this inverse problem, whether in the LaMET or related frameworks like the short-distance factorization. We also address a misconception that, with available lattice methods, the LaMET framework allows a “direct” computation of the 𝑥-dependence, whereas the alternative short-distance factorization only gives access to moments or fits of the 𝑥-dependence.

Dutrieux, Hervé [Aix-Marseille Université, Marseil↗
DOE OSTI2026

Large-momentum effective theory’s asymptotic extrapolation vs the inverse problem

Large-momentum effective theory is a physics-guided systematic expansion to calculate light-cone parton distributions, including collinear (PDFs) and transverse-momentum-dependent ones, at any fixed momentum fraction 𝑥 within a range of [𝑥 min , 𝑥 max ]. It theoretically solves the ill-posed inverse problem that afflicts other theoretical approaches to collinear PDFs, such as short-distance factorizations. Recently, Dutrieux et al. raised practical concerns about whether current or even future lattice data will have sufficient precision in the subasymptotic correlation region to support an error-controlled extrapolation—and if not, whether it becomes an inverse problem where the relevant uncertainties cannot be properly quantified. While we agree that not all current lattice data have the desired precision to qualify for an asymptotic extrapolation, some calculations do, and more are expected in the future. We comment on the analysis and results in Dutrieux et al. and argue that a physics-based systematic extrapolation still provides the most reliable error estimates, even when the data quality is not ideal. In contrast, reframing the long-distance asymptotic extrapolation as a data-driven-only inverse problem with ad hoc mathematical conditioning could lead to unnecessarily conservative errors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Machine learning inversion from scattering for mechanically driven polymers

A machine learning inversion method is developed for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration and off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the machine learning inversion. Finally, we train a Gaussian process regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters.

Gaussian process regressors↗
DOE OSTI2020

Electroluminescence Characterization of Recombination in Back Junction Silicon Heterojunction Test Structures: Role of the Inversion Layer

Electroluminescence (EL) characterization technique is used to quantify recombination properties in back junction silicon (Si) heterojunction (HJ) solar cell test structures with interdigitated back contact (IBC) emitters and metal contacts. These test structures are three terminal devices that allow the selective biasing and injection of minority carriers into spatially isolated emitter strips having different widths. This allows the study of minority carriers' current transport, flow, and recombination in these regions, which can, then, be applied to full IBC cells. EL is used to verify that emission is proportional to the minority carriers' concentration in the corresponding regions of the device, thus providing information relevant to minority carriers' collection when compared with laser beam induced current technique in considerably less time and with a higher spatial resolution. Current transport through the inversion layer that occurs due to the band bending at the n crystalline Si and p amorphous Si interface is discussed relying on the distance that minority carriers travel in different configurations and on their diffusion length. Laser defect spots (LDS) were, then, introduced in the emitter regions to analyze the spatial profile of minority carriers as they recombine at the LDS due to their high surface recombination velocity. Ideality factors in different configurations are spatially mapped from EL before and after introducing LDS and are used to show the effect of the inversion layer on the different recombination mechanisms. Current transport through the inversion layer is studied at different biasing conditions and correlated with current transport after LDS.

42 ENGINEERING↗
DOE OSTI2021

Inversion, chemical complexity, and interstitial transport in spinels

Abstract Spinels with the generic chemical formula AB 2 O 4 have potential applications in nuclear energy and batteries. In both cases, their functionality is related to mass transport through the crystal. Here, using long‐time atomistic simulations, we examine the impact of the cation structure on interstitial transport in two spinel chemistries, inverse MgGa 2 O 4 and double MgAlGaO 4 . We emphasize two aspects of the transport properties: the unit mechanisms that are described by individual barriers, for which we introduce pole‐figure‐like plots, and the aggregate behavior of those unit mechanisms. Compared to previous work on normal spinels, we find that inversion significantly reduces the rate of interstitial transport in these structures and has an impact on the stability of defects as they move through the lattice. In particular, B cation interstitials are found to be kinetically stable only in the inverse MgGa 2 O 4 . These results provide new insight into relationship between structure, chemistry, and transport in spinels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗