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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Coordination-Induced N–H Bond Weakening in a Molybdenum Pyrrolidine Complex: Isotopic Labeling Provides Insight into the Pathway for H 2 Evolution

The synthesis and characterization of a cationic molybdenum pyrrolidine complex is described that exhibits significant coordination-induced N–H bond weakening. Here, the N–H bond dissociation free energy (BDFE) of the coordinated pyrrolidine in [( Ph Tpy)(PPh 2 Me) 2 Mo(NH(pyrr))][BArF 24 ] ([1-NH(pyrr)] + ; PhTpy = 4'-Ph-2,2',6',2''-terpyridine, NH(pyrr) = pyrrolidine, ArF 24 = [C 6 H 3 -3,5-(CF 3 ) 2 ] 4 ) was determined to be between 41–51 kcal mol -1 by thermochemical analysis and supported by a density functional theory (DFT) computed value of 48 kcal mol -1 . The complex [1-NH(pyrr)] + underwent proton-coupled electron transfer (PCET) to 2,4,6-tri-tert-butylphenoxyl radical, as well as spontaneous H 2 evolution upon gentle heating to furnish the corresponding molybdenum pyrrolidide complex, [( Ph Tpy)(PPh 2 Me) 2 Mo(N(pyrr))][BArF 24 ] ([1-N(pyrr)] + ). Thermolysis of the deuterated isotopolog, [1-ND(pyrr)] + still produced H 2 with concomitant incorporation of the isotopic label into the pyrrolidide ligand in the product [(1-N(pyrr-d n )] + (n = 0–2), consistent with an H 2 evolution pathway involving intramolecular H–H bond formation followed by an intermolecular product-forming PCET step. These observations provide context for understanding H 2 evolution in the nonclassical ammine complex [( Ph Tpy)(PPh 2 Me) 2 Mo(NH 3 )][BArF 24 ] ([1-NH 3 ] + ) and are supported by DFT-computed reaction thermochemistry. Overall, these studies offer rare insight into the H 2 formation pathway in nonclassical amine complexes with N–H BDFEs below the thermodynamic threshold for H 2 evolution and inform the development of well-defined, thermody-namically potent PCET reagents.

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

Intermetallic Nanocatalysts from Heterobimetallic Group 10–14 Pyridine-2-thiolate Precursors

Intermetallic compounds are atomically ordered inorganic materials containing two or more transition metals and main-group elements in unique crystal structures. Intermetallics based on group 10 and group 14 metals have shown enhanced activity, selectivity, and durability in comparison to simple metals and alloys in many catalytic reactions. While high-temperature solid-state methods to prepare intermetallic compounds exist, softer synthetic methods can provide key advantages, such as enabling the preparation of metastable phases or of smaller particles with increased surface areas for catalysis. In this work, we study a generalized family of heterobimetallic precursors to binary intermetallics, each containing a group 10 metal and a group 14 tetrel bonded together and supported by pincer-like pyridine-2-thiolate ligands. Upon thermal decomposition, these heterobimetallic complexes form 10–14 binary intermetallic nanocrystals. Experiments and density functional theory (DFT) computations help in better understanding the reactivity of these precursors toward the synthesis of specific intermetallic binary phases. Using Pd 2 Sn as an example, we demonstrate that nanoparticles made in this way can act as uniquely selective catalysts for the reduction of nitroarenes to azoxyarenes, which highlights the utility of the intermetallics made by our method. Employing heterobimetallic pincer complexes as precursors toward binary nanocrystals and other metal-rich intermetallics provides opportunities to explore the fundamental chemistry and applications of these materials.

10-14 intermetallics↗
DOE OSTI2021

Control of monodomain polymer-stabilized cuboidal nanocrystals of chiral nematics by confinement.

Liquid crystals are important components of optical technologies. Cuboidal crystals consisting of chiral liquid crystals-the so-called blue phases (BPs), are of particular interest due to their crystalline structures and fast response times, but it is critical that control be gained over their phase behavior as well as the underlying dislocations and grain boundaries that arise in such systems. Blue phases exhibit cubic crystalline symmetries with lattice parameters in the 100 nm range and a network of disclination lines that can be polymerized to widen the range of temperatures over which they occur. Here, we introduce the concept of strain-controlled polymerization of BPs under confinement, which enables formation of strain-correlated stabilized morphologies that, under some circumstances, can adopt perfect single-crystal monodomain structures and undergo reversible crystal-to-crystal transformations, even if their disclination lines are polymerized. We have used super-resolution laser confocal microscopy to reveal the periodic structure and the lattice planes of the strain and polymerization stabilized BPs in 3D real space. Our experimental observations are supported and interpreted by relying on theory and computational simulations in terms of a free energy functional for a tensorial order parameter. Simulations are used to determine the orientation of the lattice planes unambiguously. The findings presented here offer opportunities for engineering optical devices based on single-crystal, polymer-stabilized BPs whose inherent liquid nature, fast dynamics, and long-range crystalline order can be fully exploited.

confinement↗
DOE OSTI2024

Nanocrystal Assemblies: Current Advances and Open Problems

Here we explore the potential of nanocrystals (a term used equivalently to nanoparticles) as building blocks for nanomaterials, and the current advances and open challenges for fundamental science developments and applications. Nanocrystal assemblies are inherently multiscale, and the generation of revolutionary material properties requires a precise understanding of the relationship between structure and function, the former being determined by classical effects and the latter often by quantum effects. With an emphasis on theory and computation, we discuss challenges that hamper current assembly strategies and to what extent nanocrystal assemblies represent thermodynamic equilibrium or kinetically trapped metastable states. We also examine dynamic effects and optimization of assembly protocols. Finally, we discuss promising material functions and examples of their realization with nanocrystal assemblies.

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

Highly Conjugated Porphyrin Arrays Enable Optical Resolution of Ferromagnetic and Antiferromagnetic Aligned States of the Triplet Exciton and an Incorporated Stable Radical

Well-defined photogenerated molecular spin systems have potential utility in spintronics and quantum information science (QIS). Because molecular magnetic, optical, and electronic properties can be controlled by design, diverse spin systems can be prepared at modest temperatures. Photogenerated molecular spin systems often involve states prepared from the interaction of excitons and charges. Resolving the nature of electron spin alignment in photogenerated spin states described by the coupling of a triplet exciton and a stable radical commonly relies on EPR spectroscopy. Here, we describe ethyne-bridged (porphinato)metal ( PM n ) oligomers that incorporate a macrocycle-bound Cu(II) radical center. Upon photoexcitation of such PM n arrays, a singdoublet ( 2 S 1 ) state is formed; ultrafast internal conversion (IC) then produces a tripdoublet ( 2 T 1 ) state, which undergoes intersystem crossing (ISC) to produce a tripquartet ( 4 T 1 ) state, before relaxation to the ground state ( 2 S 0 ). These highly conjugated Cu(II) radical-containing PMn arrays enable direct observation of copper porphyrin 2 T 1 → 4 T 1 ISC dynamics from the biexponential decay of the near-infrared (NIR) 2,4 T 1 → 2,4 T n transient absorption manifold. Multireference n-electron valence perturbation theory (NEVPT2) computations illuminate how PM n electronic structure controls the relaxation dynamics of these long-lived (>10 ns) electronically excited multiplet states. These studies show that highly conjugated and polarizable porphyrin arrays incorporating stable spin centers provide rare π-delocalized systems where the ferromagnetic and antiferromagnetic alignment between a triplet exciton and a stable radical are both spectrally resolved and addressable using transient optical spectroscopy at wavelengths exceeding 1 μm, providing new opportunities to QIS.

14 SOLAR ENERGY↗
DOE OSTI2020

Detecting and tracking drift in quantum information processors

Abstract If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The learnability of Pauli noise

Recently, several quantum benchmarking algorithms have been developed to characterize noisy quantum gates on today’s quantum devices. A fundamental issue in benchmarking is that not everything about quantum noise is learnable due to the existence of gauge freedom, leaving open the question what information is learnable and what is not, which is unclear even for a single CNOT gate. Here we give a precise characterization of the learnability of Pauli noise channels attached to Clifford gates using graph theoretical tools. Our results reveal the optimality of cycle benchmarking in the sense that it can extract all learnable information about Pauli noise. We experimentally demonstrate noise characterization of IBM’s CNOT gate up to 2 unlearnable degrees of freedom, for which we obtain bounds using physical constraints. In addition, we show that an attempt to extract unlearnable information by ignoring state preparation noise yields unphysical estimates, which is used to lower bound the state preparation noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery

Abstract Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as powerful inverse design tools, often functioning in discrete action spaces. This constrains their application in materials design problems, which involve continuous search spaces. Here, we introduce an RL-based framework CASTING (Continuous Action Space Tree Search for inverse design), that employs a decision tree-based Monte Carlo Tree Search (MCTS) algorithm with continuous space adaptation through modified policies and sampling. Using representative examples like Silver (Ag) for metals, Carbon (C) for covalent systems, and multicomponent systems such as graphane, boron nitride, and complex correlated oxides, we showcase its accuracy, convergence speed, and scalability in materials discovery and design. Furthermore, with the inverse design of super-hard Carbon phases, we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Estimating the randomness of quantum circuit ensembles up to 50 qubits

Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information. The ability of random circuits to approximate any random unitaries has consequences on their complexity, expressibility, and trainability. To study this property of random circuits, we develop numerical protocols for estimating the frame potential, the distance between a given ensemble and the exact randomness. Our tensor-network-based algorithm has polynomial complexity for shallow circuits and is high-performing using CPU and GPU parallelism. We study 1. local and parallel random circuits to verify the linear growth in complexity as stated by the Brown–Susskind conjecture, and; 2. hardware-efficient ansätze to shed light on its expressibility and the barren plateau problem in the context of variational algorithms. Our work shows that large-scale tensor network simulations could provide important hints toward open problems in quantum information science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Boundaries of quantum supremacy via random circuit sampling

Abstract Google’s quantum supremacy experiment heralded a transition point where quantum computers can evaluate a computational task, random circuit sampling, faster than classical supercomputers. We examine the constraints on the region of quantum advantage for quantum circuits with a larger number of qubits and gates than experimentally implemented. At near-term gate fidelities, we demonstrate that quantum supremacy is limited to circuits with a qubit count and circuit depth of a few hundred. Larger circuits encounter two distinct boundaries: a return of a classical advantage and practically infeasible quantum runtimes. Decreasing error rates cause the region of a quantum advantage to grow rapidly. At error rates required for early implementations of the surface code, the largest circuit size within the quantum supremacy regime coincides approximately with the smallest circuit size needed to implement error correction. Thus, the boundaries of quantum supremacy may fortuitously coincide with the advent of scalable, error-corrected quantum computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantum reservoir computing implementation on coherently coupled quantum oscillators

Quantum reservoir computing is a promising approach for quantum neural networks, capable of solving hard learning tasks on both classical and quantum input data. However, current approaches with qubits suffer from limited connectivity. We propose an implementation for quantum reservoir that obtains a large number of densely connected neurons by using parametrically coupled quantum oscillators instead of physically coupled qubits. We analyze a specific hardware implementation based on superconducting circuits: with just two coupled quantum oscillators, we create a quantum reservoir comprising up to 81 neurons. We obtain state-of-the-art accuracy of 99% on benchmark tasks that otherwise require at least 24 classical oscillators to be solved. Our results give the coupling and dissipation requirements in the system and show how they affect the performance of the quantum reservoir. Beyond quantum reservoir computing, the use of parametrically coupled bosonic modes holds promise for realizing large quantum neural network architectures, with billions of neurons implemented with only 10 coupled quantum oscillators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Artificial intelligence-driven approaches for materials design and discovery

Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial and error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence, have reshaped the landscape of designing new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. Here, in this Review, we present key computational advances in materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning methods and evolutionary algorithms, to advanced artificial intelligence strategies such as reinforcement learning and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This Review may serve as a brief guide to the approaches, progress and outlook of designing future functional materials with technological relevance.

computational methods↗
DOE OSTI2022

Black carbon aerosol number and mass concentration measurements by picosecond short-range elastic backscatter lidar

Abstract Black carbon aerosol emissions are recognized as contributors to global warming and air pollution. There remains, however, a lack of techniques to remotely measure black carbon aerosol particles with high range and time resolution. This article presents a direct and contact-free remote technique to estimate the black carbon aerosol number and mass concentration at a few meters from the emission source. This is done using the Colibri instrument based on a novel technique, referred to here as Picosecond Short-Range Elastic Backscatter Lidar (PSR-EBL). To address the complexity of retrieving lidar products at short measurement ranges, we apply a forward inversion method featuring radiometric lidar calibration. Our method is based on an extension of a well-established light-scattering model, the Rayleigh–Debye–Gans for Fractal-Aggregates (RDG-FA) theory, which computes an analytical expression of lidar parameters. These parameters are the backscattering cross-sections and the lidar ratio for black carbon fractal aggregates. Using a small-scale Jet A-1 kerosene pool fire, we demonstrate the ability of the technique to quantify the aerosol number and mass concentration with centimetre range-resolution and millisecond time-resolution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Complexity-calibrated benchmarks for machine learning reveal when prediction algorithms succeed and mislead

Abstract Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form of recurrent neural network. Recently, a “next-generation” reservoir computer was introduced in which the memory trace involves only a finite number of previous symbols. We explore the inherent limitations of finite-past memory traces in this intriguing proposal. A lower bound from Fano’s inequality shows that, on highly non-Markovian processes generated by large probabilistic state machines, next-generation reservoir computers with reasonably long memory traces have an error probability that is at least $$\sim 60\%$$ ∼ 60 % higher than the minimal attainable error probability in predicting the next observation. More generally, it appears that popular recurrent neural networks fall far short of optimally predicting such complex processes. These results highlight the need for a new generation of optimized recurrent neural network architectures. Alongside this finding, we present concentration-of-measure results for randomly-generated but complex processes. One conclusion is that large probabilistic state machines—specifically, large $$\epsilon$$ ϵ -machines—are key to generating challenging and structurally-unbiased stimuli for ground-truthing recurrent neural network architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Molecular dynamics simulations for glass transition temperature predictions of polyhydroxyalkanoate biopolymers

Polyhydroxyalkanoates (PHAs) represent an emerging class of biosynthetic and biodegradable polyesters that exhibit considerable potential to replace petroleum-based plastics towards a sustainable future. Despite the promise, general structure–property mappings within this class of polymers remain largely unexplored. An efficient exploration of this vast chemical space calls for the development and validation of predictive methods for accurate estimation of a diverse range of properties for PHA-based polymers. Towards this aim, we present and validate in this work the results of our molecular dynamics (MD) simulation based approach aimed at predicting glass transition temperatures (T g ) of PHA-based polymers. Since generally available and widely used polymer forcefields exhibit a relatively poor performance for T g predictions, we have developed a new forcefield by modifying the polymer consistent force field (PCFF) via refining a selected set of torsion potentials of the polymer backbone using accurate density functional theory (DFT) computations. After carefully assessing the dependence of critical simulation parameters, such as, polymer chain length, number of polymer chains, supercell size, and thermal quenching rate used in the simulation, the applicability and transferability of the modified PCFF (mPCFF) is demonstrated by directly comparing the computed T g predictions of various polymers with different chemistries, polymer side chain lengths and functional groups forming the polymer side chains against the respective experimentally measured values. Furthermore, the transport properties such as self-diffusion coefficient and viscosity are computationally determined and their well-known correlation with the target properties is demonstrated. Lastly, we have employed the developed approach to predict T g values for a number of yet-to-be-synthesized PHA-based polymers with a diverse set of functional groups in the polymer side chains. The results are further rationalized by correlating the predicted T g values with the inter-chain H-bond formation tendencies of the different side chain functional groups. This work represents an important first step towards computationally guided design of PHA-based functional polymers and opens up new directions for a systematic investigation of composition- and configuration-dependent structure–property relationships in more complex binary and ternary copolymer systems.

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

Molecular growth upon ionization of van der Waals clusters containing HCCH and HCN is a pathway to prebiotic molecules

The growth mechanisms of organic molecules in an ionizing environment such as the interstellar medium are not completely understood. Here we examine by means of ab initio molecular dynamics (AIMD) simulations and density functional theory (DFT) computations the possibility of bond formation and molecular growth upon ionization of van der Waals clusters of pure HCN clusters, and mixed clusters of HCN and HCCH, both of which are widespread in the interstellar medium. Ionization of van der Waals clusters can potentially lead to growth in low temperature and low-density environments. Our results show, that upon ionization of the pure HCN clusters, strongly bound stable structures are formed that contain N∙∙∙H bonds, and growth beyond pairwise HCN molecules is seen only in a small percentage of cases. In contrast, mixed clusters, where HCCH is preferentially ionized over HCN, can grow up to 3 or 4 units long with new carbon–carbon and carbon–nitrogen covalent bonds. Moreover, cyclic molecules formed, such as the radical cation of pyridine, which is a prebiotic molecule. The results presented here are significant as they provide a feasible pathway for molecular growth of small organic molecules containing both carbon and nitrogen in cold and relatively denser environments such as in dense molecular clouds but closer to the photo-dissociation regions, and protoplanetary disks. In the mechanism we propose, first, a neutral van der Waals cluster is formed. Once the cluster is formed it can undergo photoionization which leads to chemical reactivity without any reaction barrier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗