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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enzyme property prediction using artificial intelligence

Artificial intelligence (AI)-driven enzyme property prediction enables rapid discovery and engineering of enzymes for a wide range of biotechnological and therapeutic applications. Here, we first introduce the key components in AI model development, including enzyme datasets, protein representation methods, and model architectures. We then highlight a variety of AI tools developed for the prediction of enzyme properties and functional annotations, including enzyme structure, kinetic parameters, substrate specificity, thermostability, solubility, Enzyme Commission number, and Gene Ontology term. Moreover, we describe representative downstream applications enabled by these AI tools. Finally, we discuss some challenges and opportunities as well as future prospects.

Yuan, Le [University of Illinois at Urbana-Champai↗
DOE OSTI2024

Shielding development for the spallation neutron source VENUS instrument

VENUS is a neutron imaging instrument that will use a broad range of neutron wavelengths, from epithermal to cold energies, and will include enhanced contrast mechanisms. It will offer novel energy-selective imaging techniques directly connecting complex engineering materials and systems’ structures, properties, and functions to reveal practical and fundamental answers about their real-world performance. Here, the instrument will be built at SNS beamline 10, facing the decoupled poisoned hydrogen moderator. The driving cost for the instrument is the beamline and instrument cave shielding. Final analyses were performed to evaluate the thickness and composition of shielding materials for the instrument cave and beamline to meet radiation safety criteria for the instrument to start up in 2024 after completing the SNS proton power upgrade.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Five-body systems with Bethe-Salpeter equations

We extend the Bethe-Salpeter formalism to systems made of five valence particles. Restricting ourselves to two-body interactions, we derive the subtraction terms necessary to prevent overcounting. We solve the five-body Bethe-Salpeter equation numerically for a system of five scalar particles interacting by a scalar exchange boson. To make the calculations tractable, we implement properties of the permutation group S 5 and construct an approximation based on intermediate two- and three-body poles. We extract the five-body ground and excited states along with the spectra obtained from the two-, three-, and four-body equations. In the limit of a massless exchange particle, the two-, three, four- and five-body states coexist within a certain range of the coupling strength, whereas for heavier exchange particles the five-body system becomes Borromean. Our study serves as a building block for the calculation of pentaquark properties using functional methods.

Eichmann, Gernot [Univ. of Graz (Austria)] (ORCID:↗
DOE OSTI2021

Strain-Induced Anion-Site Occupancy in Perovskite Oxyfluoride Films

Anion ordering is a promising route to engineer physical properties in functional heteroanionic materials. A central challenge in the study of anion-ordered compounds lies in developing robust synthetic strategies to control anion occupation and in understanding the resultant implications for electronic structure. Here, we show that epitaxial strain induces preferential occupation of F and O on the anion sites in perovskite oxyfluorides SrMn O2.5-δ F γ films grown on different substrates. Under compressive strain, F tends to occupy the apical-like sites, which was revealed by F and O K-edge linearly polarized x-ray absorption spectroscopy and density functional theory calculations, resulting in an enhanced c-axis expansion. Under tensile strain, F tends to occupy the equatorial-like sites, enabling the longer Mn-F bonds to lie within the plane. The oxyfluoride films exhibit a significant orbital polarization of the 3d electrons, distinct F-site dependence to their valence band density of states, and an enhanced resistivity when F occupies the apical-like anion site compared to the equatorial-like site. By demonstrating a general strategy for inducing preferential anion-site occupancy in oxyfluoride perovskites, this work lays the foundation for future materials design and synthesis efforts that leverage this greater degree of atomic control to realize new polar or quasi-two-dimensional materials.

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

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature’s own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and “smart” architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Furthermore, continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

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

Wet Brush Homopolymers as “Smart Solvents” for Rapid, Large Period Block Copolymer Thin Film Self-Assembly

The spontaneous formation of periodic nanopatterns through the self-assembly of block copolymer (BCP) thin films offers an enticing path to engineer functional material properties over large areas, but the prohibitively slow ordering of ultrahigh molecular weight BCPs that generate large pattern periods (>100 nm) severely restricts the design space in which BCPs may be used. Here, we demonstrate that blending lamellae-forming polystyrene-block-poly(methyl methacrylate) diblock copolymers with very low molecular weight polystyrene and poly(methyl methacrylate) homopolymers can reduce by an order-of-magnitude the times required to form ordered large period patterns through solvent vapor annealing. Using in situ grazing-incidence small-angle X-ray scattering and ex situ electron microscopy, we show that the added homopolymer plasticizers act as a “smart solvent”: They increase the mobility of the long BCP chains by amplifying the dilution effect during solvent vapor annealing but segregate to like domains during subsequent thermal annealing to produce high-quality patterns with periods that may exceed 200 nm, which are readily transferred to other materials. The dramatic impact of homopolymer plasticizers may help extend the use of BCPs into new areas, particularly for visible light photonic applications that demand larger period nanopatterns.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Elastomer Mechanics of Cross-Linked Linear-Ring Polymer Blends

Cross-linking a blend of linear and ring polymers creates a new topology-based dual-network elastomer in which the two components differ significantly in their topology. We use molecular simulations and topological analysis to examine key mechanical properties as functions of ring polymer volume fraction Φ R . For Φ R < Φ R *, where the rings begin to overlap, the network shear modulus G and the maximum stretch ratio λ p are weakly dependent on Φ R . For Φ R > Φ R *, entanglements trapped in the network are diluted as the rings overlap, leading to a significant decrease in G and an increase in λ p with increasing Φ R . Here, the peak tensile stress, σ p , exhibits a maximum around Φ R *, indicating an enhancement of network strength due to the stronger cohesion from the entanglements between linear and ring polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Colloidal Dispersions of Sterically and Electrostatically Stabilized PbS Quantum Dots: Structure Factors, Second Virial Coefficients, and Film-Forming Properties

Electrostatically stabilized nanocrystals (NCs) and, in particular, quantum dots (QDs) hold promise for forming strongly coupled superlattices due to their compact and electronically conductive surface ligands. However, studies of the colloidal dispersion and interparticle interactions of electrostatically stabilized sub-10 nm NCs have been limited, hindering the optimization of their colloidal stability and self-assembly. In this study, we employed small-angle X-ray scattering (SAXS) experiments to investigate the interparticle interactions and arrangement of PbS QDs with thiostannate ligands (PbS-Sn2S64-) in polar solvents. The study reveals significant deviations from the ideal solution behavior in electrostatically stabilized QD dispersions. Our results demonstrate that PbS-Sn2S64- QDs exhibit long-range interactions within the solvent, in contrast to the short-range steric repulsion characteristic of PbS QDs with oleate ligands (PbS-OA). Introducing highly charged multivalent electrolytes screens electrostatic interactions between charged QDs, reducing the length scale of the repulsive interactions. Furthermore, we calculated the second virial (B2) coefficients from SAXS data, providing insights into how surface chemistry, solvent, and size influence pair potentials. Finally, we explore the influence of long-range interparticle interactions of PbS-Sn2S64- QDs on the morphology of films produced by drying or spin-coating colloidal solutions. The long-range repulsive term of PbS-Sn2S64- QDs promotes the formation of amorphous films, and screening the electrostatic repulsion by the addition of an electrolyte enables the formation of crystalline domains. These findings highlight the critical role of NC-NC interactions in tailoring the properties of functional materials made of colloidal NCs.

SAXS↗
DOE OSTI2026

Ultrafast Nanoimaging of Carrier Funneling in Composition-Graded Semiconductor Nanowires

Recent advances in bandgap engineering of low-dimensional semiconductors have enabled high-efficiency carrier transport in miniaturized electronic and optoelectronic devices. The physical properties and functionalities of these materials are governed by complex carrier dynamics coupled with multiple transport mechanisms in tailored band structures. Here, we report ultrafast nanoimaging of carrier funneling and recombination in composition-grade CdSxSe1-x nanowires using pump-probe near-field nanoscopy. Leveraging the high resolution of this technique in both space and time, we resolve nanoscale local carrier dynamics along composition-graded nanowires, revealing the local variation of composition-dependent carrier mobilities and lifetimes that significantly differ from their uniform composition counterparts. Furthermore, we demonstrate a length-dependent behavior wherein shorter nanowires exhibit enhanced funneling effects, accelerating carrier transport by up to 33%. Our findings provide direct visualization of nanoscale carrier transport while supporting an effective approach for investigating complex carrier interactions in inhomogeneous semiconductor nanostructures, with implications for optimizing next-generation optoelectronic devices.

Yang, Rundi↗
DOE OSTI2020

Revealing High Na-Content P2-Type Layered Oxides as Advanced Sodium-Ion Cathodes

Layered Na-based oxides with the general composition of Na x TMO 2 (TM: transition metal) have attracted significant attention for their high compositional diversity that provides tunable electrochemical performance for electrodes in sodium-ion batteries. The various compositions bring forward complex structural chemistry that is decisive for the layered stacking structure, Na-ion conductivity, and the redox activity, potentially promising new avenues in functional material properties. In this work, we have explored the maximum Na content in P2-type layered oxides and discovered that the high-content Na in the host enhances the structural stability; moreover, it promotes the oxidation of low-valent cations to their high oxidation states (in this case Ni 2+ ). This can be rationalized by the increased hybridization of the O(2p)-TM(3d-e g *) states, affecting both the local TM environment as well as the interactions between the NaO 2 and TMO 2 layers. These properties are highly beneficial for the Na storage capabilities as required for cathode materials in sodium-ion batteries. It leads to excellent Na-ion mobility, a large storage capacity (>100 mAh g –1 between 2.0-4.0 V), yet preventing the detrimental sliding of the TMO 2 layers (P2–O2 structural transition), as reflected by the ultralong cycle life (3000 (dis)charge cycles demonstrated). These findings expand the horizons of high Na-content P2-type materials, providing new insights of the electronic and structural chemistry for advanced cathode materials.

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

Uncertainty-aware mixed-variable machine learning for materials design

Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Impact of CuInP 2 S 6 –metal interfaces on the stabilization of polar phases and polarization switching

The multifunctionality of two-dimensional ferroelectric CuInP 2 S 6 (CIPS) arises from the existence of multiple polar phases combined with a high ionic conductivity that facilitates polarization switching in unusual ways. The van der Waals (vdW) layered structure provides ultrathin flakes and ideal interfaces to integrate with other materials for microelectronics and neuromorphic elements. However, device integration necessitates metal contacts to read, write, or transmit signals. In this work, we find that different types of metal–CIPS interfaces strongly impact the stabilization of specific polar phases and the field-induced transitions between the polarization states. Cu electrodes initially suppress the piezoresponse, whereas, at CIPS–Ag interfaces, the electromechanical signal is increased. Under electric fields, the Cu electrodes, Ag electrodes and surrounding CIPS surfaces can show distinct switching behavior as different phases and polarization orientations are stabilized. These findings highlight that metal–CIPS interfaces provide the opportunity to optimize functional material properties.

Ferroelectrics and multiferroics↗
DOE OSTI2021

Decorated networks of native proteins: nanomaterials with tunable mesoscopic domain size

Natural and artificial proteins with designer properties and functionalities offer unparalleled opportunity for functional nanoarchitectures formed through self-assembly. However, to exploit this potential we need to design the system such that assembly results in desired architecture forms while avoiding denaturation and therefore retaining protein functionality. Here we address this challenge with a model system of fluorescent proteins. By manipulating self-assembly using techniques inspired by soft matter where interactions between the components are controlled to yield the desired structure, we have developed a methodology to assemble networks of proteins of one species which we can decorate with another, whose coverage we can tune. Consequently, the interfaces between domains of each component can also be tuned, with potential applications for example in energy – or electron – transfer. Our model system of eGFP and mCherry with tuneable interactions reveals control over domain sizes in the resulting networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Generative AI for design of nanoporous materials: review and future prospects

Generative artificial intelligence (AI) is emerging as a powerful tool for advancing the design of nanoporous materials such as metal–organic frameworks, covalent–organic frameworks, and zeolites. These materials have potential application in important areas such as carbon capture, catalysis, gas storage, chemical separation, and drug delivery due to their modular, tunable structures, and their performance in these areas depends on precise control over their structure, chemical functionalities, and properties. Herein, we provide a review of generative AI algorithms that are emerging as powerful tools for the design of nanoporous materials, namely generative adversarial networks, variational autoencoders, diffusion models, genetic algorithms, reinforcement learning, and large language models. Some models are particularly good at generating diverse and high-quality designs, while others excel at exploring large design spaces or optimizing materials with desired properties. Certain algorithms also allow for efficient transitions between different designs, and some offer versatility in generating materials based on textual input. We discuss the advantages, limitations, and applications of these algorithms in porous material design and emphasize the future potential of integrating AI with experimental workflows to accelerate the development and validation of AI-generated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tailoring magnetization reversal of a single-domain bar nanomagnet via its end geometry

Nanoscale single-domain bar magnets are building blocks for a variety of fundamental and applied mesoscopic magnetic systems, such as artificial spin ices, magnetic shape-morphing microbots, and magnetic majority logic gates. The magnetization reversal switching field of the bar nanomagnets is a crucial parameter that determines the physical properties and functionalities of their constituted artificial systems. Previous methods on tuning the magnetization reversal switching field of a bar nanomagnet usually relied on modifying its aspect ratio, such as its length, width, and/or thickness. Here, we show that the switching field of a bar nanomagnet saturates when extending its length beyond a certain value, preventing further tailoring of the magnetization reversal via aspect ratios. We showcase a highly tunable switching field of a bar nanomagnet by tailoring its end geometry without altering its size. This provides an easy method to control the magnetization reversal of a single-domain bar nanomagnet. It would enable new research and/or applications, such as designing artificial spin ices with additional tuning parameters, engineering magnetic microbots with more flexibility, and developing magnetic quantum-dot cellular automata systems for low power computing.

36 MATERIALS SCIENCE↗