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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Magnetic Ordering in a High‐Spin Donor–Acceptor Conjugated Polymer

Abstract The development of open‐shell organic molecules that magnetically order at room temperature,which can be practically applied, remains a grand challenge in chemistry, physics, and materials science. Despite the exploration of vast chemical space, design paradigms for organic paramagnetic centers generally result in unpaired electron spins that are unstable or isotropic. Here, a high‐spin conjugated polymer is demonstrated, which is composed of alternating cyclopentadithiophene and benzo[1,2‐ c ;4,5‐ c ′]bis[1,2,5]thiadiazole heterocycles, in which macromolecular structure and topology coalesce to promote the spin center generation and intermolecular exchange coupling. Electron paramagnetic resonance (EPR) spectroscopy is consistent with spatially localized spins, while magnetic susceptibility measurements show clear anisotropic spin ordering and exchange interactions that persist at room temperature. The application of long‐range π‐correlations for spin center generation promotes remarkable stability. This work offers a fundamentally new approach to the implementation of this long‐sought‐after physical phenomenon within organic materials and the integration of manifold properties within emerging technologies.

Steelman, Michael E.↗
DOE OSTI2023

Exciton Fine Structure in 2D Perovskites: The Out‐of‐Plane Excitonic State

2D Ruddlesden‐Popper metal‐halide perovskites feature particularly strong excitonic effects, making them a fascinating playground for studying exciton physics. A complete understanding of the properties of this quasi‐particle is crucial to fully exploit the tremendous potential of 2D perovskites (2DP) in light emission applications. Despite intense investigations, some of the exciton properties remain elusive to date, for example, the energy‐ordering of the exciton states within the so‐called fine structure manifold. Using optical spectroscopy, it demonstrates that in the archetypical 2DP (PEA) 2 PbI 4 , in contradiction to theoretical predictions, the energy of the bright out‐of‐plane exciton state is higher than that of two in‐plane states. Having elucidated the order of exciton fine structure, it determines the g ‐factor of the dark exciton transition, together with the values of the electron and hole g ‐factors in the direction parallel to the c ‐axis of the crystal. In this way, it provides for the first time, a complete picture of the exciton fine structure in (PEA) 2 PbI 4 2DP.

2D perovskites↗
DOE OSTI2022

Understanding the Role of Order in Y‐Series Non‐Fullerene Solar Cells to Realize High Open‐Circuit Voltages

Abstract Non‐fullerene acceptors (NFAs) as used in state‐of‐the‐art organic solar cells feature highly crystalline layers that go along with low energetic disorder. Here, the crucial role of energetic disorder in blends of the donor polymer PM6 with two Y‐series NFAs, Y6, and N4 is studied. By performing temperature‐dependent charge transport and recombination studies, a consistent picture of the shape of the density of state distributions for free charges in the two blends is developed, allowing an analytical description of the dependence of the open‐circuit voltage V OC on temperature and illumination intensity. Disorder is found to influence the value of the V OC at room temperature, but also its progression with temperature. Here, the PM6:Y6 blend benefits substantially from its narrower state distributions. The analysis also shows that the energy of the equilibrated free charge population is well below the energy of the NFA singlet excitons for both blends and possibly below the energy of the populated charge transfer manifold, indicating a down‐hill driving force for free charge formation. It is concluded that energetic disorder of charge‐separated states has to be considered in the analysis of the photovoltaic properties, even for the more ordered PM6:Y6 blend.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Regioselective Arene C−H Alkylation Enabled by Organic Photoredox Catalysis

Abstract Expanding the toolbox of C−H functionalization reactions applicable to the late‐stage modification of complex molecules is of interest in medicinal chemistry, wherein the preparation of structural variants of known pharmacophores is a key strategy for drug development. One manifold for the functionalization of aromatic molecules utilizes diazo compounds and a transition‐metal catalyst to generate a metallocarbene species, which is capable of direct insertion into an aromatic C−H bond. However, these high‐energy intermediates can often require directing groups or a large excess of substrate to achieve efficient and selective reactivity. Herein, we report that arene cation radicals generated by organic photoredox catalysis engage in formal C−H functionalization reactions with diazoacetate derivatives, furnishing sp 2 –sp 3 coupled products with moderate‐to‐good regioselectivity. In contrast to previous methods utilizing metallocarbene intermediates, this transformation does not proceed via a carbene intermediate, nor does it require the presence of a transition‐metal catalyst.

Holmberg‐Douglas, Natalie↗
DOE OSTI2020

Regioselective Arene C−H Alkylation Enabled by Organic Photoredox Catalysis

Abstract Expanding the toolbox of C−H functionalization reactions applicable to the late‐stage modification of complex molecules is of interest in medicinal chemistry, wherein the preparation of structural variants of known pharmacophores is a key strategy for drug development. One manifold for the functionalization of aromatic molecules utilizes diazo compounds and a transition‐metal catalyst to generate a metallocarbene species, which is capable of direct insertion into an aromatic C−H bond. However, these high‐energy intermediates can often require directing groups or a large excess of substrate to achieve efficient and selective reactivity. Herein, we report that arene cation radicals generated by organic photoredox catalysis engage in formal C−H functionalization reactions with diazoacetate derivatives, furnishing sp 2 –sp 3 coupled products with moderate‐to‐good regioselectivity. In contrast to previous methods utilizing metallocarbene intermediates, this transformation does not proceed via a carbene intermediate, nor does it require the presence of a transition‐metal catalyst.

Holmberg‐Douglas, Natalie↗
DOE OSTI2020

Probabilistic learning and updating of a digital twin for composite material systems

This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.

42 ENGINEERING↗
DOE OSTI2023

Probabilistic partition of unity networks for high–dimensional regression problems

We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction. With the proposed framework, the target function is approximated by a mixture of experts model on a low-dimensional manifold, where each cluster is associated with a fixed-degree polynomial. We present a training strategy that leverages the expectation maximization (EM) algorithm. During the training, we alternate between (i) applying gradient descent to update the DNN coefficients; and (ii) using closed-form formulae derived from the EM algorithm to update the mixture of experts model parameters. Under the probabilistic formulation, step (ii) admits the form of embarrassingly paralleliazable weighted least-squares solves. The PPOU-Nets consistently outperform the baseline fully-connected neural networks of comparable sizes in numerical experiments of various data dimensions. Here, we also explore the proposed model in applications of quantum computing, where the PPOU-Nets act as surrogate models for cost landscapes associated with variational quantum circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A general framework for substructuring‐based domain decomposition methods for models having nonlocal interactions

Abstract A mathematical framework is provided for a substructuring‐based domain decomposition (DD) approach for nonlocal problems that features interactions between points separated by a finite distance. Here, by substructuring it is meant that a traditional geometric configuration for local partial differential equation (PDE) problems is used in which a computational domain is subdivided into non‐overlapping subdomains. In the nonlocal setting, this approach is substructuring‐based in the sense that those subdomains interact with neighboring domains over interface regions having finite volume, in contrast to the local PDE setting in which interfaces are lower dimensional manifolds separating abutting subdomains. Key results include the equivalence between the global, single‐domain nonlocal problem and its multi‐domain reformulation, both at the continuous and discrete levels. These results provide the rigorous foundation necessary for the development of efficient solution strategies for nonlocal DD methods.

Capodaglio, Giacomo↗
DOE OSTI2026

Reduced‐Order Modeling of Energetic Materials Using Physics‐Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)

Physics-aware deep learning (PADL) has gained popularity for use in spatiotemporal dynamics simulations, such as those in computational modeling of energetic materials (EM). We show that the challenge PADL methods face while learning complex field evolution problems can be simplified and accelerated by decoupling it into two tasks: learning complex geometric features in evolving fields and modeling dynamics over these features in a lower-dimensional feature space. We build upon our previous work on physics-aware recurrent convolutional neural networks (PARC). PARC embeds knowledge of underlying physics into its neural network architecture for more robust and accurate prediction of evolving physical fields. PARC was shown to effectively learn complex nonlinear features such as the formation of hotspots and coupled shock fronts in various initiation scenarios of EMs, as a function of microstructures, serving effectively as a microstructure-aware burn model. Here, we further accelerate PARC and reduce its computational cost by projecting the original dynamics onto a lower-dimensional invariant manifold, or “latent space.” The projected latent representation encodes the complex geometry of evolving fields (e.g., temperature and pressure) in a set of data-driven features. The reduced dimension of this latent space allows us to learn the dynamics during the initiation of EM with a lighter and more efficient model. We observe a significant decrease in training and inference time while maintaining results comparable to PARC at inference. This work takes steps towards enabling rapid prediction of EM thermomechanics at larger scales and characterization of EM structure–property–performance linkages at a full application scale.

Mathematics and Computing↗
DOE OSTI2020

Machine Learning Calabi–Yau Metrics

We apply machine learning to the problem of finding numerical Calabi–Yau metrics. Building on Donaldson's algorithm for calculating balanced metrics on Kähler manifolds, we combine conventional curve fitting and machine-learning techniques to numerically approximate Ricci-flat metrics. We show that machine learning is able to predict the Calabi–Yau metric and quantities associated with it, such as its determinant, having seen only a small sample of training data. Using this in conjunction with a straightforward curve fitting routine, we demonstrate that it is possible to find highly accurate numerical metrics much more quickly than by using Donaldson's algorithm alone, with our new machine-learning algorithm decreasing the time required by between one and two orders of magnitude.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Adaptive Variational Quantum Imaginary Time Evolution Approach for Ground State Preparation

Abstract An adaptive variational quantum imaginary time evolution (AVQITE) approach is introduced that yields efficient representations of ground states for interacting Hamiltonians on near‐term quantum computers. It is based on McLachlan's variational principle applied to imaginary time evolution of variational wave functions. The variational parameters evolve deterministically according to equations of motions that minimize the difference to the exact imaginary time evolution, which is quantified by the McLachlan distance. Rather than working with a fixed variational ansatz, where the McLachlan distance is constrained by the quality of the ansatz, the AVQITE method iteratively expands the ansatz along the dynamical path to keep the McLachlan distance below a chosen threshold. This ensures the state is able to follow the quantum imaginary time evolution path in the system Hilbert space rather than in a restricted variational manifold set by a predefined fixed ansatz. AVQITE is used to prepare ground states of H 4 , H 2 O, and BeH 2 molecules, where it yields compact variational ansätze and ground state energies within chemical accuracy. Polynomial scaling of circuit depth with system size is shown through a set of AVQITE calculations of quantum spin models. Finally, quantum Lanczos calculations are demonstrated alongside AVQITE without additional quantum resource costs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Dimensionality reduction using elastic measures

With the recent surge in big data analytics for hyperdimensional data, there is a renewed interest in dimensionality reduction techniques. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked, and metrics are typically chosen without consideration of the underlying geometry of the data. Here, in this paper, we present a method for incorporating elastic metrics into the t-distributed stochastic neighbour embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method, we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG-7, Car data set and Plane data set of Thankoor), where we achieve 0.77, 0.95 and 1.00 F1 score, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Quantum complexity of time evolution with chaotic Hamiltonians

We study the quantum complexity of time evolution in large-N chaotic systems, with the SYK model as our main example. This complexity is expected to increase linearly for exponential time prior to saturating at its maximum value, and is related to the length of minimal geodesics on the manifold of unitary operators that act on Hilbert space. Using the Euler-Arnold formalism, we demonstrate that there is always a geodesic between the identity and the time evolution operator e -iHt whose length grows linearly with time. This geodesic is minimal until there is an obstruction to its minimality, after which it can fail to be a minimum either locally or globally. We identify a criterion — the Eigenstate Complexity Hypothesis (ECH) — which bounds the overlap between offdiagonal energy eigenstate projectors and the k-local operators of the theory, and use it to argue that the linear geodesic will at least be a local minimum for exponential time. We show numerically that the large-N SYK model (which is chaotic) satisfies ECH and thus has no local obstructions to linear growth of complexity for exponential time, as expected from holographic duality. In contrast, we also study the case with N = 2 fermions (which is integrable) and find short-time linear complexity growth followed by oscillations. Our analysis relates complexity to familiar properties of physical theories like their spectra and the structure of energy eigenstates and has implications for the hypothesized computational complexity class separations PSPACE * BQP/poly and PSPACE * BQSUBEXP/subexp, and the “fast-forwarding” of quantum Hamiltonians.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Gauged supergravity from the Lunin-Maldacena background

We show how to expand the Lunin-Maldacena solution to the full bosonic sector of gauged N = 2 supergravity. In particular, we construct a consistent truncation of IIB supergravity on a β-deformed AdS 5 × S 5 background retaining a dynamical metric and graviphoton in five dimensions. We comment on the relation between this solution and similar constructions on Sasaki-Einstein manifolds, as well as the possibility of further consistent truncations which generalize the Lunin-Maldacena solution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Discreteness and integrality in Conformal Field Theory

Various observables in compact CFTs are required to obey positivity, discreteness, and integrality. Positivity forms the crux of the conformal bootstrap, but understanding of the abstract implications of discreteness and integrality for the space of CFTs is lacking. We systematically study these constraints in two-dimensional, non-holomorphic CFTs, making use of two main mathematical results. First, we prove a theorem constraining the behavior near the cusp of integral, vector-valued modular functions. Second, we explicitly construct non-factorizable, non-holomorphic cuspidal functions satisfying discreteness and integrality, and prove the non-existence of such functions once positivity is added. Application of these results yields several bootstrap-type bounds on OPE data of both rational and irrational CFTs, including some powerful bounds for theories with conformal manifolds, as well as insights into questions of spectral determinacy. We prove that in rational CFT, the spectrum of operator twists t ≥ c/12 is uniquely determined by its complement. Likewise, we argue that in generic CFTs, the spectrum of operator dimensions Δ > c–1/12 is uniquely determined by its complement, absent fine-tuning in a sense we articulate. Finally, we discuss implications for black hole physics and the (non-)uniqueness of a possible ensemble interpretation of AdS 3 gravity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Renormalization of the Standard Model Effective Field Theory from geometry

S-matrix elements are invariant under field redefinitions of the Lagrangian. They are determined by geometric quantities such as the curvature of the field-space manifold of scalar and gauge fields. We present a formalism where scalar and gauge fields are treated together, with a metric on the combined space of both types of fields. Scalar and gauge scattering amplitudes are given by the Riemann curvature Rijkl of this combined space, with indices i, j, k, l chosen to be scalar or gauge indices depending on the type of external particle. One-loop divergences can also be computed in terms of geometric invariants of the combined space, which greatly simplifies the computation of renormalization group equations. We apply our formalism to the Standard Model Effective Field Theory (SMEFT), and compute the renormalization group equations for even-parity bosonic operators to mass dimension eight.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Constraints on a generalization of geometric quantum mechanics from neutrino and B0-$$ \overline{B^0} $$ oscillations

Abstract Nambu Quantum Mechanics, proposed in Phys. Lett. B536, 305 (2002), is a deformation of canonical Quantum Mechanics in which the manifold over which the “phase” of an energy eigenstate time evolves is modified. This generalization affects oscillation and interference phenomena through the introduction of two deformation parameters that quantify the extent of deviation from canonical Quantum Mechanics. In this paper, we constrain these parameters utilizing atmospheric neutrino oscillation data, andB 0 -$$ \overline{B^0} $$ B 0 ¯ oscillation data from Belle. Surprisingly, the bound from atmospheric neutrinos is stronger than the bound from Belle. Various features of Nambu Quantum Mechanics are also discussed.

Physics↗
DOE OSTI2024

Rounding out the story of higher derivative consistent truncations

At the two-derivative order, the group manifold reduction of heterotic supergravity on S 3 results in a half-maximal 7D gauged supergravity coupled to three vector multiplets, and a further truncation can be taken to remove the vector multiplets. We demonstrate that this truncation remains consistent at the four-derivative level; we do so both by analysis of the equations of motion and the supersymmetry variations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗