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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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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

OmniXAS: A universal deep-learning framework for materials x-ray absorption spectra

X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. Here, we address these challenges with OmniXAS, a framework that contains a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on the K-edge spectra database covering eight 3⁢d transition metals (Ti–Cu). The OmniXAS framework is built upon three distinct strategies. First, we use M3GNet [Nat. Comput. Sci. 2, 718 (2022)] to derive latent representations of the local chemical environment of absorption sites as input for XAS prediction, achieving significant improvements over conventional featurization techniques. Second, we employ a hierarchical transfer learning strategy, training a universal multitask model across elements before fine-tuning for element-specific predictions. Models based on this cascaded approach after elementwise fine-tuning outperform element-specific models by up to 69%. Third, we implement cross-fidelity transfer learning, adapting a universal model to predict spectra generated by simulation of a different fidelity with a much higher computational cost. This approach improves prediction accuracy by up to 11% over models trained on the target fidelity alone. Our approach significantly boosts the throughput of XAS modeling by orders of magnitude as compared to first-principles simulations and is extendable to XAS prediction for a broader range of elements. The proposed transfer learning framework is generalizable to enhance deep-learning models that target other properties in materials research.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantum-classical embedding via ghost Gutzwiller approximation for enhanced simulations of correlated electron systems

Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources. Quantum embedding methods offer a promising route by reducing computational complexity through the mapping of bulk systems onto effective impurity models, allowing more feasible simulations on pre- and early-fault-tolerant quantum devices. Here, this work develops a quantum-classical embedding framework based on the ghost Gutzwiller approximation to enable quantum-enhanced simulations of ground-state properties and spectral functions of correlated electron systems. Circuit complexity is analyzed using an adaptive variational quantum algorithm on a statevector simulator, applied to the infinite-dimensional Hubbard model with increasing ghost mode numbers from 3 to 5, resulting in circuit depths growing from 16 to 104. Noise effects are examined using a realistic error model, revealing significant impact on the spectral weight of the Hubbard bands. To mitigate these effects, the Iceberg quantum error detection code is employed, achieving up to 40% error reduction in simulations. Finally, the accuracy of the density matrix estimation and the derived spectral function is benchmarked on IBM and Quantinuum quantum hardware, featuring distinct qubit-connectivity and employing multiple levels of error mitigation techniques.

Chen, I-Chi [Ames Laboratory (AMES), Ames, IA (Uni↗
DOE OSTI2025

Absence of Long-Range Magnetic Ordering in a Trirutile High-Entropy Oxide (Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 Cu 0.2 )Ta 1.92 O 6–δ

Functionalities of solid-state materials are usually considered to be dependent on their crystal structures. The limited structural types observed in the emerging high-entropy oxides put constraints on the exploration of their physical properties and potential applications. Herein, we synthesized the first high-entropy oxide in a trirutile structure, (Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 Cu 0.2 )Ta 1.92 O 6–δ , and investigated its magnetism. The phase purity and high-entropy nature were confirmed by powder Xray diffraction and energy-dispersive spectroscopy, respectively. X-ray photoelectron spectroscopy indicated divalent Mn, Co, Ni, and Cu along with trivalent Fe. Magnetic property measurements showed antiferromagnetic coupling and potential short-range magnetic ordering below ~4 K. The temperature-dependent heat capacity data measured under zero and high magnetic fields confirmed the lack of long-range magnetic ordering and a possible low-temperature phonon excitation. The discovery of the first trirutile high-entropy oxide opens a new pathway for studying the relationship between the highly disordered atomic arrangement and magnetic interaction. Furthermore, it provides a new direction for exploring the functionalities of highentropy oxides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Composite anyons on a torus

An adiabatic approach put forward by Greiter and Wilczek interpolates between the integer quantum Hall effects of electrons and composite fermions by varying the statistical flux bound to electrons continuously from zero to an even integer number of flux quanta, such that the intermediate states represent anyons in an external magnetic field with the same “effective” integer filling factor. We consider such anyons on a torus, and construct representative wave functions for their ground as well as excited states. These wave functions involve higher Landau levels in general, but can be explicitly projected into the lowest Landau level for many parameters. We calculate the variational energy gap between the first excited state and ground state and find that it remains open as the statistical phase is varied. Lastly, we obtain from these wave functions, both analytically and numerically, various topological quantities, such as ground-state degeneracy, the Chern number, and the Hall viscosity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2025

On-Demand Selection of the Latent Domain Orientation in Spray-Deposited Block Copolymer Thin Films

With their ability to self-assemble spontaneously into well-defined nanoscale morphologies, block copolymer (BCP) thin films are a versatile platform to fabricate functional nanomaterials. An important challenge to wider deployment of BCPs in nanofabrication is combining precise control over the nanoscale domain orientation in BCP assemblies with scalable deposition techniques that are applicable to large-area, curved, and flexible substrates. Here, in this study, we show that spray-deposited smooth films of a nominally disordered BCP exhibit latent orientations, which can be prescriptively selected by controlling solvent evaporation during spray casting. Subsequent solvent vapor annealing triggers assembly toward highly ordered cylindrical morphologies along the pathway determined by solvent evaporation in the prior spray deposition stage. Faster evaporation promotes assembly of vertically oriented cylinders spanning the entire film thickness (100–300 nm). In comparison, slow solvent evaporation permits intermicellar aggregation and incipient cylinder formation in solution, which induces horizontal cylinder assembly upon annealing. The evaporatively controlled latent orientation mechanism presented herein elucidates how nonequilibrium phenomena during casting govern successive self-assembly pathways and facilitates a versatile method to dictate the domain orientation of BCP thin films on demand on flexible and highly curved substrates or in distinct pattern areas on the same substrate.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Exact-two-component block-localized wave function: A simple scheme for the automatic computation of relativistic ΔSCF

Block-localized wave function is a useful method for optimizing constrained determinants. In this article, we extend the generalized block-localized wave function technique to relativistic two-component framework. Optimization of excited state determinants for two-component wave functions presents a unique challenge because the excited state manifold is often quite dense with degenerate states. Furthermore, we test the degree to which certain symmetries result naturally from the ΔSCF optimization such as time reversal symmetry and symmetry with respect to the total angular momentum operator on a series of atomic systems. Variational optimizations may often break symmetry in order to lower the overall energy, just as unrestricted Hartree-Fock breaks spin symmetry. Overall, we demonstrate that time reversal symmetry is roughly maintained when using Hartree-Fock, but less so when using Kohn-Sham density functional theory. Additionally, maintaining total angular momentum symmetry appears to be system dependent and not guaranteed. Finally, we were able to trace the breaking of total angular momentum symmetry to the relaxation of core electrons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Polymer-Derived Amorphous Aluminosilicate Nanomembranes for H 2 Purification

Aluminosilicate zeolite membranes with robust microporous crystalline structures are attractive for the molecular separation of H 2 from light gases, but their large-scale fabrication is complicated and costly, hindering their practical applications. Herein, we present polymer-derived amorphous aluminosilicate nanomembranes that combine the exceptional processability of polymers with the superior gas separation properties of aluminosilicates. Specifically, thin-film composite membranes comprising 150 nm polydimethylsiloxane were first treated with oxygen plasma to generate 10 nm polyorganosilica (POSi) on the surface, which were then subjected to few-cycle atomic layer deposition (ALD) using trimethylaluminum as a metal precursor and water vapor as a coreactant. This scalable two-step process yields few-nanometer amorphous aluminosilicates with strong size-sieving ability. For example, three-cycle ALD treatment of POSi increases H 2 /CO 2 selectivity from 39 to 200 and H 2 /CH 4 selectivity from 190 to 500, while decreasing H 2 permeance from 990 to 210 GPU at 150 °C, superior to the state-of-the-art membranes. In conclusion, rapid and scalable manufacturing of amorphous aluminosilicate nanolayers can also be of interest for catalysis and adsorption applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

From Optimization to Sampling Through Gradient Flows

Optimization and sampling algorithms play a central role in science and engineering as they enable finding optimal predictions, policies, and recommendations, as well as expected and equilibrium states of complex systems. The notion of “optimality” is formalized by the choice of an objective function, while the notion of an “expected” state is specified by a probabilistic model for the distribution of states. Optimizing rugged objective functions and sampling multimodal distributions is computationally challenging, especially in high-dimensional problems. Here, for this reason, many optimization and sampling methods have been developed by researchers working in disparate fields such as Bayesian statistics, molecular dynamics, genetics, quantum chemistry, machine learning, weather forecasting, econometrics, and medical imaging.

Trillos, N. García↗
DOE OSTI2020

Relativistic density functional theory in the full potential linear muffin tin orbital method

In this write-up, we have outlined the general methodology for treating relativity in the FP-LMTO method. The Schrodinger equation is insufficient for describing the physics of heavy elements because it leaves out any relativistic effects. Therefore DFT based on the Schrodinger equation is not sufficient for studying heavy materials. It is, however, possible to incorporate all of the effects of relativity in DFT if starting with a reformulation of the Hohenberg-Kohn theorem that proceeds from the Dirac equation, rather than the Schrodinger equation. The corresponding theorem in relativistic density functional theory (RDFT) shows that the ground-state density of the system is a functional of not only the charge density as in Schrodinger-based DFT, but also of the current density. This leads to a more elaborate procedure in practice when solving the Dirac-Kohn-Sham equations, the relativistic analogs of the Kohn-Sham equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Understanding the Benefit of Hybrid Electrolytes towards Vanadium Dissolution Suppression and Improved Capacity Retention in Zinc‐Aqueous Batteries Using NaV 3 O 8 Cathodes

Vanadate cathodes used in aqueous Zn-ion batteries with ZnSO 4 are hindered by capacity loss from V dissolution into the electrolyte. However, studies pinpointing the onset of dissolution as a function of electrochemical redox state and quantifying the amount of associated active material are lacking. To prevent dissolution of the NaV 3 O 8 active material, Na + ions are introduced into the electrolyte. Specifically, a hybrid ZnSO 4 + Na 2 SO 4 electrolyte is investigated in concert with NaV 3 O 8 (NVO) cathodes of varied crystallinity to determine the resulting impacts on cathode dissolution and functional electrochemistry. The use of Na + -containing hybrid electrolyte shows no significant change in Zn 2+ diffusion coefficients yet improved capacity retention. Time-resolved quantitative optical emission spectroscopy demonstrates the suppression of V dissolution with the hybrid electrolyte in both pristine and cycled electrodes. Operando synchrotron X-ray diffraction and absorption provide mechanistic insights. Hydrated NVO with wider interplanar spacing exhibits much higher H + /Zn 2+ capacity, while the Na 2 SO 4 mitigates the formation of irreversible side products. Furthermore, this study demonstrates that the use of hybrid electrolytes and control of crystallite size in the parent material can significantly improve electrochemical behavior of layered V-based cathodes in Zn-ion batteries, providing a general strategy toward safe and resilient aqueous battery systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A universal and facile approach for building multifunctional conjugated polymers for human-integrated electronics

Polymer semiconductors have shown distinct promise for the development of human- integrated electronics, owing to their solution processability and mechanical softness. However, numerous functional properties required for this application domain face synthetic challenges to be imparted onto conjugated polymers and thus combined with efficient charge- transport property. Here, we develop a “click-to-polymer” (CLIP) synthesis strategy for conjugated polymers, which uses a click reaction for the facile and versatile attachment of diverse types of functional units to a pre-synthesized conjugated-polymer precursor. With four types of functional groups, we show that functionalized polymers from this CLIP method can still retain good charge-carrier mobility. Here, we take two realized polymers to showcase the photo-patternable property and biochemical sensing function, both of which advance the state of the art of realizing these two types of functions on conjugated polymers. We expect the expanded use of this synthesis approach can largely enrich the functional properties from conjugated polymers.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reactivity of Sulfur Vacancy-Rich MoS 2 to Water Dissociation

Exposed Mo atoms on the surface of MoS 2 can catalyze certain useful chemical reactions, including the dissociation of water to produce hydrogen. However, a comprehensive understanding of water reactivity on defective MoS 2 surfaces remains elusive. Here, we use in situ near ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) to investigate water dissociation reactions on MoS 2 surfaces before and after Ar + ion beam bombardment. To make the surfaces reactive, we treated them with Ar + ion beam sputtering first, which created exposed Mo sites. Using ultraviolet photoelectron spectroscopy (UPS) and density of states calculations conducted using density functional theory (DFT), we verified that stripping the surface of sulfur atoms creates metallic surface states that can catalyze water dissociation. At elevated H 2 O pressures, XPS measurements combined with DFT calculations suggested the presence of four distinct surface species from dissociated water. Specifically, we found that oxides and hydroxides are prominent at the surface, while chemisorbed and physisorbed H 2 O molecules are also present. In conclusion, this study provides new insights that reveal the prospects of surface-engineered MoS 2 as a catalyst for water dissociation.

08 HYDROGEN↗
DOE OSTI2026

Statistical White-Line Analysis in High-Throughput TXM-XANES for Chemical State Quantification

The transmission X-ray microscopy (TXM) based X-ray absorption near-edge structure (XANES) technique provides three-dimensional mapping of element-specific chemical states at nanometer-scale spatial resolution and micrometer-scale fields of view. However, compared to conventional volume-averaged XANES (VA-XANES) measurements, the inherently small voxel size in TXM-XANES leads to a lower signal-to-noise ratio, making full-spectrum analysis computationally demanding and less robust. Here, we present the structural and compositional conditions for a statistical white-line analysis framework under which chemical state information can be directly extracted from the white-line peak position in voxel spectra without the need for voxel-wise background subtraction or normalization, under well-defined structural and compositional conditions. The method is validated on layered oxide cathode materials, where low-order polynomial fitting accurately reproduces white-line features, and the extracted energy distributions correlate strongly with VA-XANES results. This statistical approach enables high-throughput, dose-efficient, and noise-robust chemical state quantification in TXM-XANES, offering broad applicability to functional materials requiring nanoscale oxidation-state mapping.

TXM↗
DOE OSTI2026

Small Molecule Inhibitor-Modulated Al 2 O 3 Atomic Layer Deposition on Monolayer MoS 2 for Controlled Nucleation

The integration of ultrathin dielectrics on two-dimensional (2D) semiconductors is essential for advancing beyond-Si electronics. However, the intrinsic inertness of van der Waals 2D basal planes remains a primary bottleneck to achieving uniform dielectric nucleation and growth. Here, in this study, we introduce a small molecule inhibitor (SMI)-modulated thermal atomic layer deposition (ALD) strategy, exemplified by aluminum oxide (Al 2 O 3 ) ALD on monolayer molybdenum disulfide (1L MoS 2 ) with acetic acid (HAc) SMI. The ABC-type sequence comprises HAc inhibitor (A), trimethylaluminum (TMA) precursor (B), and deionized H 2 O coreactant (C). In situ quartz crystal microbalance (QCM) studies reveal robust HAc adsorption on Al 2 O 3 and suppression of subsequent oxide growth on HAc-passivated surfaces. When applied to 1L MoS 2 , this inhibitory pathway enables HAc to selectively passivate nascent Al 2 O 3 nuclei formed on the MoS 2 surface, limiting their three-dimensional (3D) island coarsening and redirecting precursor adsorption toward the uncovered basal plane. Consequently, nearly continuous ultrathin (∼1.5 nm) Al 2 O 3 films are achieved on 1L MoS 2 with markedly improved uniformity compared to standard Al 2 O 3 ALD using TMA and H 2 O, as validated by atomic force microscopy (AFM), cross-sectional scanning transmission electron microscopy (STEM), and energy-dispersive X-ray spectroscopy (EDS). Density functional theory (DFT) calculations further provide atomistic insight into HAc-modulated Al 2 O 3 nucleation, corroborating the energetic preference of HAc for Al 2 O 3 over MoS 2 and attenuated TMA adsorption on HAc-passivated surfaces. Spatially resolved Raman spectroscopy also confirms that the HAc-modulated process preserves the structural integrity of 1L MoS 2 , with only minimal strain and doping perturbations observed after dielectric deposition. This SMI-modulated approach offers a broadly applicable framework for controlling ALD nucleation across various inhibitors, ALD chemistries, and 2D materials, opening opportunities for reliable dielectric integration in next-generation nanoelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes using Artificial Neural Networks

Abstract Previous efforts to understand structure‐function relationships in high ionic conductivity materials for solid state batteries have predominantly relied on density functional theory (DFT‐) based ab initio molecular dynamics (MD). Such simulations, however, are computationally demanding and cannot be reasonably applied to large systems containing more than a hundred atoms. Here, an artificial neural network (ANN) is trained to accelerate the calculation of high accuracy atomic forces and energies used during such MD simulations. After carefully training a robust ANN for four and five element systems, nearly identical lithium ion diffusivities are obtained for Li 10 GeP 2 S 12 (LGPS) when benchmarking the ANN‐MD results with DFT‐MD. Applying the ANN‐MD approach, the effect of chlorine doping on the lithium diffusivity is calculated in an LGPS‐like structure and it is found that a dopant concentration of 1.3% maximizes ionic conductivity. The optimal concentration balances the competing consequences of effective atomic radii and dielectric constants on lithium diffusion and agrees with the experimental composition. Performing simulations at the resolution necessary to model experimentally relevant and optimal concentrations would be infeasible with traditional DFT‐MD. Systems that require a large number of simulated atoms can be studied more efficiently while maintaining high accuracy with the proposed ANN‐MD framework.

Rao, Karun K.↗
DOE OSTI2024

Syntheses and crystal structures of four new d 0 transition metal tellurites

Four new d 0 transition metal tellurites, namely InVTeO 6 (1), β-InVTe 2 O 8 (2), FeVTe 2 O 8 (3), and Fe 2 MoTe 2 O 10 (4), were synthesized under hydrothermal conditions, and their crystal structures were determined using single-crystal X-ray diffraction. Compound 1 crystallizes in the noncentrosymmetric space group P2 1 2 1 2 1 (No. 19), with unit cell parameters of a = 5.0759(2) Å, b = 8.5030(3) Å, c = 11.6376(5) Å, V = 502.28(3) Å 3 , and Z = 4, while the other three compounds crystallize in centrosymmetric structures, with space group Cmca and unit cell parameters of a = 7.0633(7) Å, b = 8.962(1) Å, c = 20.162(2) Å, V = 1276.3(2) Å 3 , and Z = 8 for compound 2, space group P2 1 /n and unit cell parameters of a = 7.8901(2) Å, b = 4.9617(1) Å, c = 16.4290(3) Å, β = 93.6198(8)°, V = 641.88(2) Å 3 , and Z = 4 for compound 3, and space group Pnma and unit cell parameters of a = 8.7878(2) Å, b = 6.1327(2) Å, c = 15.2423(4) Å, V = 821.45(4) Å 3 , and Z = 4 for compound 4. Compound 3 adopts a two-dimensional (2D) sheet structure composed of FeO 6 octahedra, VO 4 tetrahedra, and TeO 4 polyhedra. The other three compounds exhibit three-dimensional (3D) framework structures: Compound 1 is built from InO 6 octahedra, VO 4 tetrahedra, TeO 4 polyhedra; Compound 2 consists of InO 6 octahedra, VO 5 square pyramids, and TeO 3 polyhedra; and Compound 4 is composed of FeO 6 octahedra, MoO 6 octahedra, and TeO 3 polyhedra. The band structures, density of states, and electron localization functions of all four compounds were calculated. The vibrational spectra were examined for compounds 1, 2, and 4. Furthermore, powder X-ray diffraction, element analysis, and thermogravimetric analysis were conducted for compound 4.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗