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DOE OSTI2021

Structure of triple perovskite BaSr 2 MgTa 2 O 9 revisited

An experimental investigation of the structure and crystal-chemistry of the triple perovskite BaSr 2 MgTa 2 O 9 is reported. Polycrystalline BaSr 2 MgTa 2 O 9 was synthesized via solid-state reaction and its structure quantitatively probed at 10 and 300 K using neutron diffraction. Monoclinic (A2/m, a 0 $\bar{b}$ – $b$ – ) and trigonal structural models (P$\bar{3}$c1, a – a – a – ) were fit to diffraction data. The former was found to correctly reproduce experimental intensities, while the latter failed to do so. This difference is attributed to the ability of the monoclinic model to accurately reflect octahedral tilting. BaSr 2 MgTa 2 O 9 features corner-sharing MgO 6 and TaO 6 octahedra with barium and strontium cations occupying cubooctahedral holes. Barium and strontium are disordered over the A site, whereas B site cations magnesium and tantalum are ordered. Finally, chemical substitution of strontium for barium in Ba 3 MgTa 2 O 9 induces tilting of MgO 6 and TaO 6 octahedra in two directions and geometric distortions of MgO 6 octahedra.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗

DOE OSTI2020

Measuring the dynamic response of the thylakoid architecture in plant leaves by electron microscopy

The performance of the photosynthesis machinery in plants, including light harvesting, electron transport, and protein repair is controlled by structural changes of the thylakoid membrane system inside the chloroplasts. In particular, the structure of the stacked grana area of thylakoid membranes is highly dynamic, changing in response to different environmental cues such as light intensity. For example, the aqueous thylakoid lumen enclosed by thylakoid membranes in grana has been documented to swell in the presence of light. However, light-induced alteration of the stromal gap in the stacked grana (partition gap) and of the unstacked stroma lamellae have not been well characterized. Light-induced changes of the entire thylakoid membrane system including the lumen in both stacked and unstacked domains as well as the partition gap are presented here, and the functional implications are discussed. This structural analysis was made possible by development of a robust semi-automated image analysis method combined with optimized plant tissue fixation techniques for transmission electron microscopy generating quantitative structural results for the analysis of thylakoid ultrastructure.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Leveraging Structured Biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis

Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal knowledge that is qualitative in nature. This manuscript proposes a general approach for querying a causal knowledge graph with a causal question and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. Here, we demonstrate the feasibility, accuracy and versatility of this approach using two case studies in systems biology. The first demonstrates the appropriateness of the underlying assumptions and the accuracy of the results. The second demonstrates the versatility of the approach by querying a knowledge base for the molecular determinants of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm and performing counterfactual inference to predict the causal effect of medical countermeasures for severely ill COVID-19 patients.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Yielding Behavior of Bottlebrush and Linear Block Copolymers

Block copolymers can exhibit a pronounced yield stress, but the impact of molecular architecture, chemistry, and self-assembly on macroscopic rheology remains poorly understood. Here, we study the linear-viscoelastic and yield-stress fluid behavior of two architectures—bottlebrush copolymers (with statistical or blocky sequences) and linear diblocks—that self-assemble into body-centered cubic (BCC) spheres and hexagonally close-packed cylinders (HEX). The dynamic properties of these polymers were probed by oscillatory frequency and amplitude sweeps at temperatures well below the order–disorder transition (TODT) to furnish insights into the yielding transition. All BCC-forming polymers have a similar signature of yielding: smaller yield strains (γ yBCC ≈ 0.053 < γ y,HEX ≈ 0.18), sharper solid–liquid transitions, and better reversibility than HEX. Statistical bottlebrushes show the most frequency-independent structural modulus (G 0 ) and no signs of defect relaxation. A simple power-law relationship captures the dependence of the normalized structural modulus (G 0 /RT) on the inter-micelle distance (d) across different architectures and morphologies [G 0 /(RT) = 1.31 × 10 4 (nm 2.6 mol/m 3 ) d –2.6 ]. These studies establish quantitative structure–property relationships that are relevant in contemporary applications, for example, extrusion-based 3D printing.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis

We show that "single-atom” catalysts (SACs) have demonstrated excellent activity and selectivity in challenging chemical transformations such as photocatalytic CO 2 reduction. For heterogeneous photocatalytic SAC systems, it is essential to obtain sufficient information of their structure at the atomic level in order to understand reaction mechanisms. In this work, a SAC was prepared by grafting a molecular cobalt catalyst on a light-absorbing carbon nitride surface. Due to the sensitivity of the X-ray absorption near edge structure (XANES) spectra to subtle variances in the Co SAC structure in reaction conditions, different machine learning (ML) methods, including principal component analysis, K-means clustering, and neural network (NN), were utilized for in situ Co XANES data analysis. As a result, we obtained quantitative structural information of the SAC nearest atomic environment thereby extending the NN-XANES approach previously demonstrated for nanoparticles and size-selective clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

3D Phase-Field Simulations of Pattern Formation During Freeze Casting

We present the results of a combined experimental and phase-field modeling study of pattern formation during freeze casting. Those studies use unidirectional freezing of simple binary liquid mixtures of water and sugars (sucrose and trehalose) in a temperature gradient, which suffice to produce hierarchical templated structures similar to those observed in more complex multi-component freeze-cast systems including lamellae, undulated ridges, and more exotic “jellyfish-like” substructures. Multiscale 3D phase-field simulations reproduce remarkably well those structures quantitatively and identify key properties of the ice-water interface that control their formation. They further reveal that lamellae form as a result of a novel symmetry-breaking secondary instability of partially faceted cellular structures and pinpoint additional secondary instability mechanisms giving rise to smaller-scale substructures.

Kaihua Ji↗

NASA NTRSDate not supplied

3D Phase-Field Simulations of Pattern Formation During Freeze Casting

Kaihua Ji↗

DOE OSTI2024

Automated Gold Nanorod Spectral Morphology Analysis Pipeline

The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often a time-consuming, iterative process. This is often due to quantitative uncertainties in the required reaction conditions and the time, resources, and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method for colloidal nanomaterial characterization. However, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis tool, called AuNR-SMA, which is a fast and accurate method to extract quantitative structural information from colloidal AuNR absorption spectra. To demonstrate the practical utility of this model, we apply it to three distinct applications. First, we demonstrate this model's utility as an automated analysis tool in a high-throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model to predict the resulting AuNR size distributions under specified reaction conditions. Third, we apply this model to spectra extracted from the literature where no size distributions are reported and impute unreported quantitative information on AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where absorption spectra are size dependent, and accurate numerical simulation of absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements, help explore the synthesis science of nanoparticles in a rational manner, or facilitate closed-loop workflows.

36 MATERIALS SCIENCE↗

NASA NTRS1987

A quantitative approach to the topology of large-scale structure

A quantitative measure of the topology of large-scale structure: the genus of density contours in a smoothed density distribution, is described and applied. For random phase (Gaussian) density fields, the mean genus per unit volume exhibits a universal dependence on threshold density, with a normalizing factor that can be calculated from the power spectrum. If large-scale structure formed from the gravitational instability of small-amplitude density fluctuations, the topology observed today on suitable scales should follow the topology in the initial conditions. The technique is illustrated by applying it to simulations of galaxy clustering in a flat universe dominated by cold dark matter. The technique is also applied to a volume-limited sample of the CfA redshift survey and to a model in which galaxies reside on the surfaces of polyhedral 'bubbles'. The topology of the evolved mass distribution and 'biased' galaxy distribution in the cold dark matter models closely matches the topology of the density fluctuations in the initial conditions. The topology of the observational sample is consistent with the random phase, cold dark matter model.

Gott, J. Richard, III↗

NASA NTRS1973

Study of an experimental technique for application to structural dynamic problems

An experimental program was conducted to determine the feasibility of using subscale plastic models to determine the response of full-scale aerospace structural components to impulsive, pyrotechnic loadings. A monocoque cylinder was impulsively loaded around the circumference of one end, causing a compressive stress wave to propagate in the axial direction. The resulting structural responses of two configurations of the cylinder (with and without a cutout) were recorded by photoelasticity, strain gages, and accelerometers. A maximum dynamic stress concentration was photoelastically determined and the accelerations calculated from strain-gage data were in good agreement with those recorded by accelerometers. It is concluded that reliable, quantitative structural response data can be obtained by the experimental techniques described in this report.

Snell, R. F.↗

DOE OSTI2024

Structure–property relations of sodium iron phosphate nuclear waste glasses: Effects of iron redox ratio and glass composition

Iron phosphate glasses, known for their exceptional chemical durability and potential applicability in nuclear waste management, have gained significant attention over the years. The structures of these glasses are complicated by the coexistence of Fe 3+ and Fe 2+ , which plays a crucial role in determining their structures and properties. Here, this work uses molecular dynamics simulations to study the structural changes in Na 2 O–Fe 2 O 3 –P 2 O 5 glasses with varying glass composition and Fe 2+ /Fe 3+ redox ratio. It was found that the redox ratio and modifier contents significantly affected the short-range and medium-range orders in the glasses. Significant changes in the local environments around P 5+ and Fe 3+ were observed, as reflected by the bond distances and coordination numbers. Na + cations are found to preferentially associate with Fe 3+ (rather than Fe 2+ ), whereas Fe 2+ has stronger association with P 5+ than Na + , confirming the structural role of Fe 2+ as a glass modifier. The disruptions in P–O–P linkages upon increasing FeO suggest that FeO causes glass depolymerization. These glasses achieved higher connectivity with increasing Fe 3+ / (Fe 3+ + Fe 2+ ) ratios, conerting phosphorous Q 2 to Q 3 units and iron Q 5 units to Q 4 units. The decrease of nonbridging oxygen fractions with increasing Fe 3+ / (Fe 3+ + Fe 2+ ) ratios, through creating P–O–Fe linkages, is the main reason of enhanced network connectivity. Quantitative structure–property relationship analyses with different structural descriptors were used to correlate with measured properties. The analyses provided valuable insights into structure–property relationships, emphasizing the importance of choosing relevant energy parameters and defining glass network connectivity, particularly in F net descriptors. It was found the Fe–O–P linkage density exhibits strong correlations to measured dissolution rates, supporting the importance of these linkages in improving the chemical durability in iron phosphate glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Operando studies reveal active Cu nanograins for CO 2 electroreduction

Carbon dioxide electroreduction facilitates the sustainable synthesis of fuels and chemicals. Although Cu enables CO 2 -to-multicarbon product (C 2+ ) conversion, the nature of the active sites under operating conditions remains elusive. Importantly, identifying active sites of high-performance Cu nanocatalysts necessitates nanoscale, time-resolved operando techniques. Here, we present a comprehensive investigation of the structural dynamics during the life cycle of Cu nanocatalysts. A 7 nm Cu nanoparticle ensemble evolves into metallic Cu nanograins during electrolysis before complete oxidation to single-crystal Cu 2 O nanocubes following post-electrolysis air exposure. Operando analytical and four-dimensional electrochemical liquid-cell scanning transmission electron microscopy shows the presence of metallic Cu nanograins under CO 2 reduction conditions. Correlated high-energy-resolution time-resolved X-ray spectroscopy suggests that metallic Cu, rich in nanograin boundaries, supports undercoordinated active sites for C–C coupling. Quantitative structure–activity correlation shows that a higher fraction of metallic Cu nanograins leads to higher C 2+ selectivity. A 7 nm Cu nanoparticle ensemble, with a unity fraction of active Cu nanograins, exhibits sixfold higher C 2+ selectivity than the 18 nm counterpart with one-third of active Cu nanograins. Importantly, the correlation of multimodal operando techniques serves as a powerful platform to advance our fundamental understanding of the complex structural evolution of nanocatalysts under electrochemical conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗

DOE OSTI2021

Regulating Exciton–Phonon Coupling to Achieve a Near‐Unity Photoluminescence Quantum Yield in One‐Dimensional Hybrid Metal Halides

Abstract Low‐dimensional hybrid metal halides are emerging as a highly promising class of single‐component white‐emitting materials for their unique broadband emission from self‐trapped excitons (STEs). Despite substantial progress in the development of these metal halides, many challenges remain to be addressed to obtain a better fundamental understanding of the structure–property relationship and realize the full potentials of this class of materials. Here, via pressure regulation, a near 100% photoluminescence quantum yield (PLQY) of broadband emission is achieved in a corrugated 1D hybrid metal halide C 5 N 2 H 16 Pb 2 Br 6 , which possesses a highly distorted structure with an initial PLQY of 10%. Compression reduces the overlap between STE states and ground state, leading to a suppressed phonon‐assisted non‐radiative decay. The PL evolution is systematically demonstrated to be controlled by the pressure‐regulated exciton–phonon coupling which can be quantified using Huang–Rhys factor S . Detailed studies of the S ‐PLQY relation for a series of 1D hybrid metal halides (C 5 N 2 H 16 Pb 2 Br 6 , C 4 N 2 H 14 PbBr 4 , C 6 N 2 H 16 PbBr 4 , and (C 6 N 2 H 16 ) 3 Pb 2 Br 10 ) reveal a quantitative structure–property relationship that regulating S factor toward 28 leads to the maximum emission.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Unsupervised Segmentation and Clustering Workflow for Efficient Processing of 4D-STEM and 5D-STEM Data

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as data dimensionality and sampling density increase, particularly for in situ scanning diffraction experiments (5D-STEM), robust segmentation of structurally consistent behavior across sequential measurements becomes essential for efficient and physically meaningful analysis. Here, we introduce a clustering framework that identifies crystallographically distinct domains from 4D-STEM datasets. By using local diffraction-pattern similarity as a metric, the method extracts closed contours delineating spatially contiguous regions. This approach produces cluster-averaged diffraction patterns that improve signal quality while reducing data volume by orders of magnitude, enabling rapid and accurate orientation, phase, and strain mapping. We demonstrate the applicability of this approach to in situ liquid-cell 4D-STEM data of gold nanoparticle growth. Our method provides a scalable and generalizable route for spatially coherent segmentation, data compression, and quantitative structure–strain mapping across diverse 4D-STEM modalities. The full analysis code and example workflows are publicly available to support reproducibility and reuse.

4D-STEM↗

NASA NTRS1994

Comparison of large-scale structures and velocities in the local universe

Comparison of the large-scale density and velocity fields in the local universe shows detailed agreement, strengthening the standard paradigm of the gravitational origin of these structures. Quantitative analysis can determine the cosmological density parameter, Omega, and biasing factor, b; there is virtually no sensitivity in any local analyses to the cosmologial constant, lambda. Comparison of the dipole anisotropy of the cosmic microwave background with the acceleration due to the Infrared Astronomy Satellite (IRAS) galaxies puts the linear growth factor in the range beta approximately equals Omega (exp 0.6)/b = 0.6(+0.7/-0.3) (95% confidence). A direct comparison of the density and velocity fields of nearby galaxies gives beta = 1.3 (+0.7/-0.6), and from nonlinear analysis the weaker limit (Omega greater than 0.45 for b greater than 0.5 (again 95% confidence). A tighter limit (Omega greater than 0.3 (4-6 sigma)), is obtained by a reconstruction of the probability distribution function of the initial fluctuations from which the structures observed today arose. The last two methods depend critically on the smooth velocity field determined from the observed velocities of nearby galaxies by the POTENT method. A new analysis of these velocities, with more than three times the data used to obtain the above quoted results, is now underway and promises to tighten the uncertainties considerably, as well as reduce systematic bias.

Yahil, Amos↗

DOE OSTI2022

Pressure Engineering Promising Transparent Oxides with Large Conductivity Enhancement and Strong Thermal Stability

Abstract Transparent conducting oxides (TCO) with high electrical conductivity and high visible light transparency are desired for a wide range of high‐impact engineering. Yet, usually, a compromise must be made between conductivity and transparency, limiting the practical application of a TCO to the next level. Furthermore, TCO performance is highly sensitive to composition, so conventional synthesis methods, such as chemical doping, cannot unravel the mysteries of the quantitative structure–performance relationship. Thus, improving the fundamental understanding or creating materials‐by‐design has limited success. Here, a strategy is proposed to modulate the lattice and electronic and optical properties precisely by applying pressure on a TCO. Strikingly, after compression–decompression treatment on the indium titanium oxides (ITiO), a highly transparent and metastable phase with two orders of magnitude enhancement in conductivity is synthesized from an irreversible phase transition. Moreover, this phase possesses previously unattainable filter efficiency on hazardous blue light up to 600 °C, providing potential for healthcare‐related applications with strong thermal stability up to 200 °C. These results demonstrate that pressure engineering is a clean and effective tool for tailoring functional materials that are not achievable by other means, providing an exciting alternative property‐tuning dimension in materials science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗