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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tunable Solid‐State Properties and Anisotropic Charge Mobility in Hydrogen‐Bonded Diketopyrrolopyrrole Polymers via Automated Device Fabrication and Characterization

The optoelectronic properties of semiconducting polymers and device performance rely on a delicate interplay of design and processing conditions. However, screening and optimizing the relationships between these parameters for reliably fabricating organic electronics can be an arduous task requiring significant time and resources. To overcome this challenge, Polybot is developed—a robotic platform within a self‐driving lab that can efficiently produce organic field‐effect transistors (OFETs) from various semiconducting polymers via high‐throughput blade coating deposition. Polybot not only handles the fabrication process but also can conduct characterization tests on the devices and autonomously analyze the data gathered, thus facilitating the rapid acquisition of data on a large scale. This work leverages the capabilities of this platform to investigate the fabrication of OFETs using hydrogen bonding‐containing semiconducting polymers. Through high‐throughput fabrication and characterization, various data trends are analyzed, and large extents of anisotropic charge mobility are observed in devices. The materials are thoroughly characterized to understand the role of processing conditions in solid state and electronic properties of these organic semiconductors. The findings demonstrate the effectiveness of automated fabrication and characterization platforms in uncovering novel structure–property relationships, facilitating refinement of rational chemical design, and processing conditions, ultimately leading to new semiconducting materials.

automated fabrication and characterization↗
DOE OSTI2024

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

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

Ternary Phosphides Ba M 2 P 2 : Tailoring Crystal and Electronic Structures Enables Highly Efficient HER Electrocatalysis

Binary transition metal phosphides and their solid solutions have emerged as promising hydrogen evolution reaction (HER) catalysts. Although many research endeavors have adopted strategies to vary compositions to optimize catalytic performance, they mainly focus on binary structures, which represent only a small fraction of the abundant phase space of structure types among transition metal phosphides. Here, the largely unexplored class of ternary and multinary ordered phosphides in catalysis comprises two or more metals with quite different chemical nature, concealing the structure–property relationships essential for advancing catalyst design. Here, we explored phosphides crystallizing in one of the most abundant ordered intermetallic structure types, —the ThCr 2 Si 2 type, —where square nets of 3d transition metal M and P atoms are separated by layers of electropositive Ba cations. Four ternary BaM 2 P 2 (M = Fe, Fe/Cu, Fe/Ni, Ni) catalysts were synthesized and characterized. BaNi 2 P 2 showed high HER activity in acidic electrolyte, which required an overpotential, η 10 , of only 62 mV to drive current density j = –10 mA/cm 2 and high stability with a potential drop rate of 0.25 mV/h. BaNi 2 P 2 outperformed other Ni-based catalysts, such as Ni 2 P and Ni 5 P 4 . Notably, at current densities above –170 mA/cm 2 , BaNi 2 P 2 outperformed the standard Pt electrode measured under identical conditions. Electronic structure analysis revealed a volcano-type activity trend among the four BaM 2 P 2 catalysts based on their d-band center positions, highlighting the role of electropositive Ba cations in shifting the Ni-3d orbitals into an optimal position.

BaNi2P2↗
DOE OSTI2025

Understanding the Active Site Structures and Achieving Catalytic Activity Tuning of Atomically Dispersed FeN 4 Sites for Oxygen Reduction Reaction

Atomically dispersed Fe–—N—C catalysts with high oxygen reduction reaction (ORR) activity have attracted great attention since the last decade. Due to comparable ORR activity and low material cost, they are promising platinum group metal (PGM)-free catalysts that can replace the commercialized Pt/C materials; furthermore, it can facilitate the efficiency of the fuel cell technologies and mitigate dependence on fossil fuels. Great advancements have been made to experimentally optimize the synthesis approach of the Fe–—N—C catalysts, enhance the ORR activity, and improve the catalyst stability. Similarly, recent theoretical studies also provide enriched understanding of the active site structures, properties, and reaction mechanisms. In this review, discussions are made upon utilizing combined experimental and computational spectroscopy to reveal the active site structures, employing mechanistic studies to investigate reaction thermodynamics and kinetics, as well as developing scaling relationships to assist the design and development of future PGM-free catalyst materials. Furthermore, recent advances in studying Fe–—N—C catalysts utilizing electrified surface models and explicit solvation models are also discussed. Not only can these aspects improve the accuracy of theoretical simulation and predictions but also deepen the understanding of the catalyst properties and reaction mechanisms under the effect of surface charges and solvent molecules.

Fe single-atom catalysts↗
DOE OSTI2026

The Influence of Shielding Gas on the Wire-Arc Additive Manufacturing (WAAM) of Ni-Based Superalloy Haynes 282

Wire-arc additive manufacturing (WAAM) enables building large near-net-shaped parts using fast deposition rates and is attractive due to the potential cost and schedule savings. Haynes® 282® is a Ni-based superalloy with wide application in advanced power generation systems for its superior high temperature mechanical properties. High-quality WAAM H282 parts are achieved through careful process optimization, hence, we have focused on systematically characterizing the processing-structure-properties relationships over a wide range of wire-feed and travel speeds using a standard Ar30He shielding gas. Here the influence of adding 0.25-3% of H2 and CO2 to Ar-30He standard on our findings is examined. Multi-bead tracks were used to screen 20+ gas combinations to examine impact on wettability, porosity, oxidation, and grain structures. Improved multi-tracks are observed for gases with up to 1% H2 and 0.25% CO2. WAAM H282 builds within this range are examined to reveal differences in microstructure as evaluated with CT, SEM-EDS, and EBSD.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Stereochemically‐Controlled Fluorinated Copolymers for Selectively Permeable Barrier Applications

Selective oxygen permeability coupled with low water vapor transmission is essential for biomedical and packaging applications requiring controlled oxygen flux under humid conditions. However, most high‐performance barrier polymers depend on perfluoroalkyl substances (PFAS), whose persistence and regulatory restrictions limit their long‐term applicability. We designed a series of stereocontrolled thiol‐yne‐based polyesters, including both fluorinated and non‐fluorinated variants, for selective oxygen permeability with considerable water barrier performance. Tailoring polymer crystallinity and morphology tuned both oxygen transport and mechanical properties. Fluorinated polymers demonstrated enhanced hydrophobicity and water resistance while maintaining oxygen diffusivity within a range relevant to oxygen‐sensing applications. Structure–property relationships were elucidated through small‐ and wide‐angle X‐ray scattering, revealing semi‐crystalline domains influenced by fluorine content and dithiol chain length. Barrier performance was rigorously evaluated via water vapor transmission rate and dynamic vapor sorption, showing reduced water uptake with increasing dithiol monomer length and crystallinity. In conclusion, this work introduces a PFAS‐free alternative to conventional barrier materials and establishes a tunable materials platform with potential relevance for biomedical devices and packaging systems requiring controlled oxygen permeability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗
DOE OSTI2024

Unconventional pressure-induced magnetic transitions and mechanical properties in non-magnetic LaFeSi

Here, this work demonstrates that two consecutive magnetic transitions occur in non-magnetic LaFeSi compound under negative hydrostatic pressure (small volume expansion). Electronic structure properties of LaFeSi were calculated using density functional theory (DFT) to understand the origin of these magnetic transitions. Mechanical properties of LaFeSi, especially elastic properties (shear and Young’s modulus, and Poisson’s ratio), were determined by DFT distortion calculations to elucidate lattice anisotropy and stability. The antiferromagnetic transition at –7.52 GPa, predicted in our work for LaFeSi, is consistent with the reported experimentally observed antiferromagnetic ground states in CeMnSi and LaMnSi compounds having similar crystal geometry. This finding of unexpected magnetic ordering predicts that layered non-magnetic materials, like LaFeSi, may become magnetically active with lattice expansion.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Coupling between collective modes in the deformed 98 Zr nucleus: Insights from consistent HFB + QRPA calculations with the Gogny interaction

The zirconium isotopes exhibit structural properties that present multiple challenges to nuclear theory. Investigations of the coupling present within isoscalar modes and within isovector modes are scarce but important for advancing our understanding of the microscopic picture of nuclei. To explore some of these underlying coupling features, and to test the predictive power of a state-of-the-art nuclear structure approach, we provide a detailed analysis of the properties of 90,96,98 Zr . This region includes a benchmarking case and offers insights into nuclear deformation phenomena. Here, to investigate the coupling between collective modes in deformed nuclei, we focused our analysis on the ground and excited-state properties of these isotopes, employing a consistent approach with the axially symmetric deformed Hartree-Fock-Bogoliubov (HFB) and the quasiparticle random phase approximation (QRPA) framework, both using the Gogny D1M force. This approach effectively describes both low-lying and giant-resonance states. We devoted special attention to the deformed 98 Zr nucleus, where we confirm the existence of coupling between monopole and quadrupole excitations through the 𝐾 𝜋 = 0 + QRPA components and demonstrate an analogous dipole-octupole coupling through the 𝐾 𝜋 = 0 − and 𝐾 𝜋 = 1 − components. Intrinsic transition densities and associated radial projections illustrate the coupling. Our work complements and extends earlier studies carried out using density-functional-based methods and notably, we included the complete Coulomb interaction also in the pairing fields, i.e., we treat terms exactly that are approximated in typical calculations that use the Gogny D1 and D2 interaction families.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Irradiation-Induced Structural Disorder and Its Influence on the Mechanical Response of Polycrystalline MoS2

Molybdenum disulfide (MoS2) thin films are widely used as dry-film lubricants and protective coatings in aerospace and other radiation-exposed environments. Conventional synthesis routes produce polycrystalline films whose grain boundaries and other native defects cause their mechanical and tribological behavior to differ substantially from that of ideal single crystals. Under irradiation, these films progressively evolve from polycrystalline structures, composed of layered MoS2 grains, into highly disordered and eventually amorphous structures, altering both their tribological performance and mechanical integrity. Here, we employ reactive atomistic simulations to investigate irradiation-driven structural evolution in bulk polycrystalline MoS2. Using controlled primary knock-on atom (PKA) events, we characterize the progressive transition from a polycrystalline microstructure to an amorphous network by tracking defect accumulation and structural disorder. We then establish how this transition modifies the dominant deformation mechanisms and the temperature-dependent tensile response. Specifically, irradiation suppresses interlayer sliding and delamination, mechanisms which facilitate the deformation of the pristine polycrystal, resulting in defect-induced hardening. Broadly, our results establish direct process–structure–property relationships linking irradiation-induced defect accumulation, microstructural evolution, deformation mechanisms, and mechanical behavior, providing an atomistic framework for understanding the structural integrity and long-term reliability of irradiated MoS2 coatings.

Moore, Daniel [Sandia National Laboratories (SNL)]↗
DOE OSTI2025

Review of Layered Transition Metal Oxide Materials for Cathodes in Sodium-Ion Batteries

The growing interest in sodium-ion batteries (SIBs) is driven by scarcity and the rising costs of lithium, coupled with the urgent need for scalable and sustainable energy storage solutions. Among various cathode materials, layered transition metal oxides have emerged as promising candidates due to their structural similarity to lithium-ion battery (LIB) counterparts and their potential to deliver high energy density at reduced costs. However, significant challenges remain, including limited capacity at high charge/discharge rates and structural instability during extended cycling. Addressing these issues is critical for advancing SIB technology toward industrial applications, particularly for large-scale energy storage systems. This review provides a comprehensive analysis of layered sodium transition metal oxides, focusing on their structural properties, electrochemical performance, and degradation mechanisms. Special attention is given to the intrinsic and extrinsic factors contributing to their instability, such as structural phase transitions, and cationic/anionic redox behavior. Additionally, recent advancements in material design strategies, including doping, surface modifications, and composite formation, are discussed to highlight the progress toward enhancing the stability and performance of these materials. This work aims to bridge the knowledge gaps and inspire further innovations in the development of high-performance cathodes for sodium-ion batteries.

Ahangari, Mehdi (ORCID:0000000345803969)↗
DOE OSTI2025

Coupling between collective modes in the deformed 98 Zr nucleus: Insights from consistent HFB+QRPA calculations with the Gogny interaction

The Zirconium isotopes exhibit structural properties that present multiple challenges to nuclear theory. Investigations of the coupling present within isoscalar modes and within isovector modes are scarce but important for advancing our understanding of the microscopic picture of nuclei. To explore some of these underlying coupling features, and to test the predictive power of a state-of the-art nuclear structure approach, we provide a detailed analysis of the properties of 90,96,98Zr. This region includes a benchmarking case and offers insights into nuclear deformation phenomena. To investigate the coupling between collective modes in deformed nuclei, we focused our analysis on the ground and excited-state properties of these isotopes, employing a consistent approach with the axially-symmetric deformed Hartree-Fock-Bogoliubov (HFB) and the Quasiparticle Random Phase Approximation (QRPA) framework, both using the Gogny D1M force. This approach effectively describes both low-lying and giant-resonance states. We devoted special attention to the deformed 98Zr nucleus, where we confirm the existence of coupling between monopole and quadrupole excitations through the K π = 0 + QRPA components and demonstrate an analogous dipole-octupole coupling through the K π = 0 − and K π = 1 − components. Intrinsic transition densities and associ ated radial projections illustrate the coupling. Our work complements and extends earlier studies carried out using density-functional-based methods and notably, we included the complete Coulomb interaction also in the pairing fields, i.e. we treat terms exactly that are approximated in typical calculations that use the Gogny D1 and D2 interaction families.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Holographic entropy inequalities and multipartite entanglement

Abstract We study holographic entropy inequalities and their structural properties by making use of a judicious grouping of terms into certain multipartite information quantities. This allows us to recast cumbersome entropic expressions into much simpler ones which share interestingly rigid structures. By performing a systematic search over some of these structures, we are able to discover more than 1800 novel entropy inequalities for six parties, thereby demonstrating that these recastings provide a fruitful generating technique for uncovering new holographic entropy inequalities. In attempting to interpret the corresponding sign-definite quantities as correlation measures, we also obtain a no-go result: the superbalance property of holographic entropy inequalities turns out to preclude them from being monotonic under partial tracing. In the process, we also comment on the geometrical significance of multipartite information quantities and present various structural relations amongst them.

Physics↗
DOE OSTI2025

Synthesis and characterization of electron beam irradiation-induced damage in polycrystalline metal thin films

High-energy physics research, industrial sterilizing, and material processing depend extensively on electron beam accelerators. Exit windows are crucial components of such electron accelerator systems, maintaining vacuum integrity inside the machine while providing mechanical strength, thermal stability, and radiation resistance at the beam-target interface. In this study, thin metallic films of Ni, Ti, Cr, and V were explored for use in electron beam exit windows, and their properties were compared with the properties of their bulk counterparts. Simulation results of metal foils predicted Ti to exhibit less beam power dissipation compared to Ni. However, Ni possesses superior mechanical and structural properties compared to Ti. The performance of these films under electron beam irradiation was examined by depositing thin layers of these films on silicon and metallic substrates using magnetron sputtering and exposing them to e-beam irradiation in a controlled setup. The deposited films were subjected to a dose of approximately 66 kGy at a beam energy of 10 MeV and characterized prior to and postexposure to the beam using field emission scanning electron microscopy, atomic force microscopy, x-ray diffraction (XRD), and nanoindentation. Particular emphasis was given to characteristics like the grain structure, surface morphology, dislocation density, and hardness. XRD patterns revealed irradiation-induced changes in peak intensities, while the crystallinity remained largely unchanged. Nanoindentation results showed that the pristine and irradiated Ti and Ni films were twice as hard when compared to bulk Ti and Ni samples, regardless of the substrate type used (Si or bulk Ti, or Ni). These results emphasize the superior mechanical properties of thin metal films compared to their bulk counterparts. In conclusion, this study advances the optimization of thin film materials for robust and efficient e-beam applications, ensuring improved durability and operational reliability.

AFM↗
DOE OSTI2024

Phonon Screening of Excitons in Atomically Thin Semiconductors

Atomically thin semiconductors, encompassing both 2D materials and quantum wells, exhibit a pronounced enhancement of excitonic effects due to geometric confinement. Consequently, these materials have become foundational platforms for the exploration and utilization of excitons. Recent ab initio studies have demonstrated that phonons can substantially screen electron-hole interactions in bulk semiconductors and strongly modify the properties of excitons. While excitonic properties of atomically thin semiconductors have been the subject of extensive theoretical investigations, the role of phonon screening on excitons in atomically thin structures remains unexplored. In this Letter, we demonstrate via ab initio GW-Bethe-Salpeter equation calculations that phonon screening can have a significant impact on optical excitations in atomically thin semiconductors. We further show that the degree of phonon screening can be tuned by structural engineering. We focus on atomically thin GaN quantum wells embedded in AlN and identify specific phonons in the surrounding material, AlN, that dramatically alter the lowest-lying exciton in monolayer GaN via screening. Our studies provide new intuition beyond standard models into the interplay among structural properties, phonon characteristics, and exciton properties in atomically thin semiconductors, and have implications for future experiments.

2-dimensional systems↗
DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Analysis of Bis(trifluoromethylsulfonyl)imide Interactions with Metal Cations Through a Chemical Informatics Approach

Nominally weakly coordinating anions are useful for modulating the solubility and chemical properties of metal complexes, but identification and analysis of the systematics of the interactions of anions with cationic metal complexes has not received the attention it deserves. Here, a chemical informatics approach is demonstrated for identifying and quantitatively analyzing the ways that the bis(trifluoromethylsulfonyl)imide anion (TFSI) can interact with metal-containing species. An open access computer program (PyCIFTer) was developed to facilitate large-scale structural analysis of TFSI-containing species by utilization of experimental atomic coordinate data from single-crystal X-ray diffraction (XRD) studies obtained from the Cambridge Structural Database (CSD). PyCIFTer establishes a three-dimensional vector space from the raw atomic coordinates, generating acyclic, undirected graphs that are used to rapidly analyze the structural properties (bond lengths and angles) of TFSI in individual structures in sequential/batch fashion. The structures are sorted by PyCIFTer into groups based on pre-set and chemically sensible criteria, affording a comprehensive and systematic view of TFSI structural chemistry. This approach avoids tedious one-at-a-time interrogation of structures, a prospect unreasonable in this case, and many others of contemporary chemical relevance; there were over 1500 structures in the CSD containing TFSI as of November 2024. The results demonstrate that TFSI only rarely binds to cations in the solid state, favoring the formation of species in which TFSI is found in cations’ outer coordination spheres. The prospect of applying PyCIFTer to other moieties is also discussed. PyCIFTer is also schematically compared to the commercial CSD Python application programming interface (API). Taken together, this work demonstrates the usefulness of modular workflows for sequential/batch analysis of structural data from XRD, an approach that appears poised to accelerate the translation of legacy structural results into new chemical insights and hypotheses.

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

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗