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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Room-temperature valley-selective emission in Si-MoSe 2 heterostructures enabled by high-quality-factor chiroptical cavities

Transition metal dichalcogenides possess valley pseudospin, enabling coupling between photon spin and electron spin for classical and quantum information processing. However, rapid valley-dephasing processes have impeded the development of scalable, high-performance valleytronic devices operating at room temperature. Here we demonstrate that a chiral resonant metasurface can enable room-temperature valley-selective emission in MoSe 2 monolayers independent of excitation polarization. This platform provides circular eigen-polarization states with a high quality factor (Q-factor) and strong chiral near-field enhancement. The fabricated Si chiral metasurfaces exhibit chiroptical resonances with Q-factors up to 450 at visible wavelengths. We reveal degrees of circular polarization (DOP) reaching a record high of 0.5 at room temperature. Our measurements show that the high DOP can be attributed to the significantly increased chiroptical local density of states, which enhances valley-specific radiative transition rates by a factor of ~13. Our work could facilitate the development of ultracompact chiral classical and quantum light sources.

42 ENGINEERING↗
DOE OSTI2022

Autonomous scanning probe microscopy investigations over WS2 and Au{111}

Abstract Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS 2 sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS 2 , Au face-centered cubic, and Au hexagonal close-packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Non-volatile electric-field control of inversion symmetry

Competition between ground states at phase boundaries can lead to significant changes in properties under stimuli, particularly when these ground states have different crystal symmetries. A key challenge is to stabilize and control the coexistence of symmetry-distinct phases. Using BiFeO 3 layers confined between layers of dielectric TbScO 3 as a model system, we stabilize the mixed-phase coexistence of centrosymmetric and non-centrosymmetric BiFeO 3 phases at room temperature with antipolar, insulating and polar semiconducting behaviour, respectively. Application of orthogonal in-plane electric (polar) fields results in reversible non-volatile interconversion between the two phases, hence removing and introducing centrosymmetry. Counterintuitively, we find that an electric field 'erases' polarization, resulting from the anisotropy in octahedral tilts introduced by the interweaving TbScO 3 layers. Consequently, this interconversion between centrosymmetric and non-centrosymmetric phases generates changes in the non-linear optical response of over three orders of magnitude, resistivity of over five orders of magnitude and control of microscopic polar order. Here our work establishes a platform for cross-functional devices that take advantage of changes in optical, electrical and ferroic responses, and demonstrates octahedral tilts as an important order parameter in materials interface design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bidirectional phonon emission in two-dimensional heterostructures triggered by ultrafast charge transfer

Photoinduced charge transfer in van der Waals heterostructures occurs on the 100 fs timescale despite weak interlayer coupling and momentum mismatch. However, little is understood about the microscopic mechanism behind this ultrafast process and the role of the lattice in mediating it. Here, using femtosecond electron diffraction, we directly visualize lattice dynamics in photoexcited heterostructures of WSe 2 /WS 2 monolayers. Following the selective excitation of WSe 2 , we measure the concurrent heating of both WSe 2 and WS 2 on a picosecond timescale—an observation that is not explained by phonon transport across the interface. Using first-principles calculations, we identify a fast channel involving an electronic state hybridized across the heterostructure, enabling phonon-assisted interlayer transfer of photoexcited electrons. Phonons are emitted in both layers on the femtosecond timescale via this channel, consistent with the simultaneous lattice heating observed experimentally. Taken together, this shows our work indicates strong electron–phonon coupling via layer-hybridized electronic states—a novel route to control energy transport across atomic junctions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Floquet–Bloch manipulation of the Dirac gap in a topological antiferromagnet

Floquet–Bloch manipulation, achieved by driving a material periodically with a laser pulse, is a method that enables the engineering of electronic and magnetic phases in solids by effectively modifying the structure of their electronic bands. However, the application of Floquet–Bloch manipulation in topological magnetic systems, particularly those with inherent disorder, remains largely unexplored. Here, in this study, we realize Floquet–Bloch manipulation of the Dirac surface-state mass of the topological antiferromagnet MnBi 2 Te 4 . Using time- and angle-resolved photoemission spectroscopy, we show that opposite helicities of mid-infrared circularly polarized light result in substantially different Dirac mass gaps in the antiferromagnetic phase, despite the equilibrium Dirac cone being massless. We explain our findings in terms of a Dirac fermion with a random mass. Our results underscore Floquet–Bloch manipulation as a powerful tool for controlling topology, even in the presence of disorder, and for uncovering properties of materials that may elude conventional probes.

42 ENGINEERING↗
DOE OSTI2026

Foundation models for atomistic simulation of chemistry and materials

Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models — pre-trained on massive datasets using transformer architectures — have revolutionized many fields. It is thus interesting to ask whether a foundation model — subject to suitable data, parameter scaling and training — could enable learned simulations of chemistry and materials. Here, in this study, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on small datasets corresponding to specific, targeted atomistic simulation tasks. We provide specific criteria for creating such large-scale MLIP foundation models, coordinated strategies for their development, evaluation and deployment, and highlight potential emergent capabilities that could transform predictive simulations in chemistry and materials science and accelerate discovery across multiple technological domains.

Yuan, Eric C.-Y. [University of California, Berkel↗
DOE OSTI2024

Temporal dynamics of the multi-omic response to endurance exercise training

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood. Here, the Molecular Transducers of Physical Activity Consortium profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository (https://motrpac-data.org/).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Prime factorization using quantum variational imaginary time evolution

The road to computing on quantum devices has been accelerated by the promises that come from using Shor’s algorithm to reduce the complexity of prime factorization. However, this promise hast not yet been realized due to noisy qubits and lack of robust error correction schemes. Here we explore a promising, alternative method for prime factorization that uses well-established techniques from variational imaginary time evolution. We create a Hamiltonian whose ground state encodes the solution to the problem and use variational techniques to evolve a state iteratively towards these prime factors. We show that the number of circuits evaluated in each iteration scales as \(O(n^{5}d)\) , where n is the bit-length of the number to be factorized and d is the depth of the circuit. We use a single layer of entangling gates to factorize 36 numbers represented using 7, 8, and 9-qubit Hamiltonians. We also verify the method’s performance by implementing it on the IBMQ Lima hardware to factorize 55, 65, 77 and 91 which are greater than the largest number (21) to have been factorized on IBMQ hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Entropy removal of medical diagnostics

Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. To date, no studies have extensively and quantitatively applied Shannon entropy in a systematic way to quantify the entropy of clinical situations using diagnostic variables (true and false positives and negatives, respectively). Decision tree representations of medical decision-making tools can be generated using diagnostic variables found in literature and entropy removal can be calculated for these tools. This concept of clinical entropy removal has significant potential for further use to bring forth healthcare innovation, such as quantifying the impact of clinical guidelines and value of care and applications to Emergency Medicine scenarios where diagnostic accuracy in a limited time window is paramount. This analysis was done for 623 diagnostic tools and provided unique insights into their utility. For studies that provided detailed data on medical decision-making algorithms, bootstrapped datasets were generated from source data to perform comprehensive machine learning analysis on these algorithms and their constituent steps, which revealed a novel and thorough evaluation of medical diagnostic algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Ultrafast optical melting of trimer superstructure in layered 1T′-TaTe2

Abstract Quasi-two-dimensional transition-metal dichalcogenides are a key platform for exploring emergent nanoscale phenomena arising from complex interactions. Access to the underlying degrees-of-freedom on their natural time scales motivates the use of advanced ultrafast probes sensitive to self-organised atomic-scale patterns. Here, we report the ultrafast investigation of TaTe 2 , which exhibits unique charge and lattice trimer order characterised by a transition upon cooling from stripe-like chains into a (3 × 3) superstructure of trimer clusters. Utilising MeV-scale ultrafast electron diffraction, we capture the photo-induced TaTe 2 structural dynamics – exposing a rapid ≈ 1.4 ps melting of its low-temperature ordered state followed by recovery via thermalisation into a hot cluster superstructure. Density-functional calculations indicate that the initial quench is triggered by intra-trimer Ta charge transfer which destabilises the clusters, unlike melting of charge density waves in other TaX 2 compounds. Our work paves the way for further exploration and ultimately rapid optical and electronic manipulation of trimer superstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sexual dimorphism and the multi-omic response to exercise training in rat subcutaneous white adipose tissue

Subcutaneous white adipose tissue (scWAT) is a dynamic storage and secretory organ that regulates systemic homeostasis, yet the impact of endurance exercise training (ExT) and sex on its molecular landscape is not fully established. Utilizing an integrative multi-omics approach, and leveraging data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we show profound sexual dimorphism in the scWAT of sedentary rats and in the dynamic response of this tissue to ExT. Specifically, the scWAT of sedentary females displays -omic signatures related to insulin signaling and adipogenesis, whereas the scWAT of sedentary males is enriched in terms related to aerobic metabolism. These sex-specific -omic signatures are preserved or amplified with ExT. Integration of multi-omic analyses with phenotypic measures identifies molecular hubs predicted to drive sexually distinct responses to training. Overall, this study underscores the powerful impact of sex on adipose tissue biology and provides a rich resource to investigate the scWAT response to ExT.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Biological underpinnings for lifelong learning machines

Biological organisms learn from interactions with their environment throughout their lifetime. For artificial systems to successfully act and adapt in the real world, it is desirable to similarly be able to learn on a continual basis. This challenge is known as lifelong learning, and remains to a large extent unsolved. In this Perspective article, we identify a set of key capabilities that artificial systems will need to achieve lifelong learning. We describe a number of biological mechanisms, both neuronal and non-neuronal, that help explain how organisms solve these challenges, and present examples of biologically inspired models and biologically plausible mechanisms that have been applied to artificial systems in the quest towards development of lifelong learning machines. We discuss opportunities to further our understanding and advance the state of the art in lifelong learning, aiming to bridge the gap between natural and artificial intelligence. It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.

Kudithipudi, Dhireesha↗
DOE OSTI2022

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Polaron absorption in aligned conjugated polymer films: breakdown of adiabatic treatments and going beyond the conventional mid-gap state model

This study provides the first experimental polarized intermolecular and intramolecular optical absorption components of field-induced polarons in regioregular poly(3-hexylthiophene-2,5-diyl), rr-P3HT, a polymer semiconductor. Highly aligned rr-P3HT thin films were prepared by a high temperature shear-alignment process that orients polymer backbones along the shearing direction. rr-P3HT in-plane molecular orientation was measured by electron diffraction, and out-of-plane orientation was measured through series of synchrotron X-ray scattering techniques. Then, with molecular orientation quantified, polarized charge modulation spectroscopy was used to probe mid-IR polaron absorption in the ℏω = 0.075 — 0.75 eV range and unambiguously assign intermolecular and intramolecular optical absorption components of hole polarons in rr-P3HT. This data represents the first experimental quantification of these polarized components and allowed long-standing theoretical predictions to be compared to experimental results. The experimental data is discrepant with predictions of polaron absorption based on an adiabatic framework that works under the Born–Oppenheimer approximation, but the data is entirely consistent with a more recent nonadiabatic treatment of absorption based on a modified Holstein Hamiltonian. This nonadiabatic treatment was used to show that both intermolecular and intramolecular polaron coherence break down at length scales significantly smaller than estimated structural coherence in either direction. Importantly, this strongly suggests that polaron delocalization is fundamentally limited by energetic disorder in rr-P3HT.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Towards an IPv6-only WLCG: More successes in reducing IPv4

The Worldwide Large Hadron Collider Computing Grid (WLCG) community’s deployment of dual-stack IPv6/IPv4 on its worldwide storage infrastructure has been very successful. Dual-stack is not, however, a viable longterm solution; the HEPiX IPv6 Working Group has focused on studying where and why IPv4 is still being used, and how to flip such traffic to IPv6. The agreed end goal is to turn IPv4 off and run IPv6-only over the wide-area network to simplify both operations and security management.This paper reports our work since the CHEP2023 conference. Firstly, we present our campaign to deploy IPv6 on CPU services and Worker Nodes, with a deadline of end of June 2024. Then, the WLCG Data Challenge (DC24) performed in February 2024 was an excellent opportunity to observe the percentage of data transfers carried by IPv6. We observed the predominance of IPv6 in data transfers during DC24 and were able to understand yet more reasons for the use of IPv4 and areas for remedial action.The paper ends with the working group’s plans for moving WLCG to “IPv6- only”. One aspect of this is the possible automated use of IPv6-only clients configured with a customer-side translator, or CLAT, together with a deployment of NAT64 using what is often known as “IPv6-Mostly”, enabling IPv6-only sites to connect to non-WLCG IPv4-only services.

Attebury, Garhan [U. Nebraska, Lincoln]↗
DOE OSTI2022

Transferability of data-driven, many-body models for CO 2 simulations in the vapor and liquid phases

Here, extending on the previous work by Riera et al. [J. Chem. Theory Comput. 16, 2246–2257 (2020)], we introduce a second generation family of data-driven many-body MB-nrg models for CO 2 and systematically assess how the strength and anisotropy of the CO 2 –CO 2 interactions affect the models’ ability to predict vapor, liquid, and vapor–liquid equilibrium properties. Building upon the many-body expansion formalism, we construct a series of MB-nrg models by fitting one-body and two-body reference energies calculated at the coupled cluster level of theory for large monomer and dimer training sets. Advancing from the first generation models, we employ the charge model 5 scheme to determine the atomic charges and systematically scale the two-body energies to obtain more accurate descriptions of vapor, liquid, and vapor–liquid equilibrium properties. Challenges in model construction arise due to the anisotropic nature and small magnitude of the interaction energies in CO 2 , calling for the necessity of highly accurate descriptions of the multidimensional energy landscape of liquid CO 2 . These findings emphasize the key role played by the training set quality in the development of transferable, data-driven models, which, accurately representing high-dimensional many-body effects, can enable predictive computer simulations of molecular fluids across the entire phase diagram.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗