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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set

A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ~0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Overall, our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantum Dot–Organic Molecule Conjugates as Hosts for Photogenerated Spin Qubit Pairs

The inherent spin polarization present in photogenerated spin-correlated radical pairs makes them promising candidates for quantum computing and quantum sensing applications. The spin states of these systems can be probed and manipulated with microwave pulses using electron paramagnetic resonance spectrometers. However, to date, there are no reports on magnetic resonance-based spin measurements of photogenerated spin-correlated radical pairs hosted on quantum dots. In the current work, we prepare dye molecule-inorganic quantum dot conjugates and show that they can produce photogenerated spin-polarized states. The dye molecule, D131, is chosen for its ability to undergo efficient charge separation, and the nanoparticle materials, ZnO quantum dots, are chosen for their promising spin properties. Transient and steady state optical spectroscopy performed on ZnO quantum dot-D131 conjugates shows that reversible photogenerated charge separation is occurring. Transient and pulsed electron paramagnetic resonance experiments are then performed on the photogenerated radical pair, which demonstrate that (1) the radical pair is polarized at moderate temperatures and well modeled by existing theories and (2) the spin states can be accessed and manipulated with microwave pulses. In conclusion, this work opens the door to a new class of promising qubit materials that can be photogenerated in polarized states and hosted by highly tailorable inorganic nanoparticles.

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

Simulating strongly correlated molecules with a superconducting quantum processor

Many of the biggest challenges in expanding the nation’s access to clean and low-cost energy resources are fundamentally chemistry or materials challenges. An important case is the development of new catalysts for the up-conversion of cheap and readily available materials such as methane or water into materials suitable for use as a fuel such as methanol or oxygen. To understand and exploit such processes, computer simulations of chemical reactions provide a natural complement to experimental studies. Unfortunately, most catalytic reactions involve so-called “strongly correlated” molecules which are notoriously difficult to study with simulation algorithms that can be executed on existing (classical) computers. The recent growth in quantum information science offers an alternative potential route for simulating these difficult systems. As a result, an increasing number of computational chemists are becoming interested in quantum computing. At the same time, quantum information scientists have identified chemistry simulation as a possible first demonstration of a quantum computer providing an improvement over a classical computer. The objective of this project is to accurately simulate strongly correlated molecules on a quantum processor. To meet the high challenges of this objective, new hybrid quantum/classical algorithms will be co-designed with advanced quantum gate developments and computed on customized quantum hardware. Some of the developed techniques will be transferable to study other molecular systems, while the project as a whole will help define better strategies for advancing the quantum simulation of matter more generally.

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

UnigeneFinder: An Automated Pipeline for Gene Calling From Transcriptome Assemblies Without a Reference Genome

ABSTRACT For most species, transcriptome data are much more readily available than genome data. Without a reference genome, gene calling is cumbersome and inaccurate because of the high degree of redundancy in de novo transcriptome assemblies. To simplify and increase the accuracy of de novo transcriptome assembly in the absence of a reference genome, we developed UnigeneFinder. Combining several clustering methods, UnigeneFinder substantially reduces the redundancy typical of raw transcriptome assemblies. This pipeline offers an effective solution to the problem of inflated transcript numbers, achieving a closer representation of the actual underlying genome. UnigeneFinder performs comparably or better, compared with existing tools, on plant species with varying genome complexities. UnigeneFinder is the only available transcriptome redundancy solution that fully automates the generation of primary transcript, coding region, and protein sequences, analogous to those available for high‐quality reference genomes. These features, coupled with the pipeline’s cross‐platform implementation, focus on automation, and an accessible, user‐friendly interface, make UnigeneFinder a useful tool for many downstream sequence‐based analyses in nonmodel organisms lacking a reference genome, including differential gene expression analysis, accurate ortholog identification, functional enrichments, and evolutionary analyses. UnigeneFinder also runs efficiently both on high‐performance computing (HPC) systems and personal computers, further reducing barriers to use.

Xue, Bo [Plant Resilience Institute Michigan State↗
DOE OSTI2020

A Python Module for Storing CCD Images with openPMD (openPMD-CCD) v0.1.0

openPMD is an open meta-data schema that provides meaning and self-description to data sets in science and engineering. The openPMD-CCD software module adds interfaces to organize camera (CCD) images in hierarchical data files. This software provides modern I/O storage formats from high-performance computing and provides bindings for integration into experimental control systems, e.g. via Python 3 and LabView 2020.

Gonsalves, AnthonyJ↗
DOE OSTI2021

Analytical derivatives of the individual state energies in ensemble density functional theory. II. Implementation on graphical processing units (GPUs)

Conical intersections control excited state reactivity, and thus, elucidating and predicting their geometric and energetic characteristics are crucial for understanding photochemistry. Locating these intersections requires accurate and efficient electronic structure methods. Unfortunately, the most accurate methods (e.g., multireference perturbation theories such as XMS-CASPT2) are computationally challenging for large molecules. The state-interaction state-averaged restricted ensemble referenced Kohn–Sham (SI-SA-REKS) method is a computationally efficient alternative. The application of SI-SA-REKS to photochemistry was previously hampered by a lack of analytical nuclear gradients and nonadiabatic coupling matrix elements. We have recently derived analytical energy derivatives for the SI-SA-REKS method and implemented the method effectively on graphical processing units. We demonstrate that our implementation gives the correct conical intersection topography and energetics for several examples. Furthermore, our implementation of SI-SA-REKS is computationally efficient, with observed sub-quadratic scaling as a function of molecular size. This demonstrates the promise of SI-SA-REKS for excited state dynamics of large molecular systems.

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

TomoPyUI : a user-friendly tool for rapid tomography alignment and reconstruction

The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data ( i.e. organizing data/metadata, applying image filters etc. ) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI , a user interface for the well known tomography data processing package TomoPy . This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI , along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI , and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

On the potentially transformative role of auxiliary-field quantum Monte Carlo in quantum chemistry: A highly accurate method for transition metals and beyond

Approximate solutions to the ab initio electronic structure problem have been a focus of theoretical and computational chemistry research for much of the past century, with the goal of predicting relevant energy differences to within “chemical accuracy” (1 kcal/mol). For small organic molecules, or in general, for weakly correlated main group chemistry, a hierarchy of single-reference wave function methods has been rigorously established, spanning perturbation theory and the coupled cluster (CC) formalism. For these systems, CC with singles, doubles, and perturbative triples is known to achieve chemical accuracy, albeit at O(N7) computational cost. In addition, a hierarchy of density functional approximations of increasing formal sophistication, known as Jacob’s ladder, has been shown to systematically reduce average errors over large datasets representing weakly correlated chemistry. However, the accuracy of such computational models is less clear in the increasingly important frontiers of chemical space including transition metals and f-block compounds, in which strong correlation can play an important role in reactivity. A stochastic method, phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC), has been shown to be capable of producing chemically accurate predictions even for challenging molecular systems beyond the main group, with relatively low O(N3 − N4) cost and near-perfect parallel efficiency. Herein, we present our perspectives on the past, present, and future of the ph-AFQMC method. We focus on its potential in transition metal quantum chemistry to be a highly accurate, systematically improvable method that can reliably probe strongly correlated systems in biology and chemical catalysis and provide reference thermochemical values (for future development of density functionals or interatomic potentials) when experiments are either noisy or absent. Finally, we discuss the present limitations of the method and where we expect near-term development to be most fruitful.

Chemistry↗
DOE OSTI2024

Side chain engineering in indacenodithiophene- co -benzothiadiazole and its impact on mixed ionic–electronic transport properties

Organic semiconductors are increasingly being decorated with hydrophilic solubilising chains to create materials that can function as mixed ionic–electronic conductors, which are promising candidates for interfacing biological systems with organic electronics. While numerous organic semiconductors, including p- and n-type materials, small molecules and polymers, have been successfully tailored to encompass mixed conduction properties, common to all these systems is that they have been semicrystalline materials. Here, we explore how side chain engineering in the nano-crystalline indacenodithiophene-co-benzothiadiazole (IDTBT) polymer can be used to instil ionic transport properties and how this in turn influences the electronic transport properties. This allows us to ultimately assess the mixed ionic–electronic transport properties of these new IDTBT polymers using the organic electrochemical transistor as the testing platform. Using a complementary experimental and computational approach, we find that polar IDTBT derivatives can be infiltrated by water and solvated ions, they can be electrochemically doped efficiently in aqueous electrolyte with fast doping kinetics, and upon aqueous swelling there is no deterioration of the close interchain contacts that are vital for efficient charge transport in the IDTBT system. Despite these promising attributes, mixed ionic–electronic charge transport properties are surprisingly poor in all the polar IDTBT derivatives. Albeit a “negative” result, this finding clearly contradicts established side chain engineering rules for mixed ionic–electronic conductors, which motivated our continued investigation of this system. We eventually find this anomalous behaviour to be caused by increasing energetic disorder in the polymers with increasing polar side chain content. We have investigated computationally how the polar side chain motifs contribute to this detrimental energetic inhomogeneity and ultimately use the learnings to propose new molecular design criteria for side chains that can facilitate ion transport without impeding electronic transport.

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

Dicyanobenzothiadiazole (DCBT) Organic Dye as a Visible Light Absorbing Strong Photoinduced Oxidant with a 16 Microsecond Long–Lived Excited State

Strong photoinduced oxidants are important to organic synthesis and solar energy conversion, to chemical fuels or electric. For these applications, visible light absorption is important to solar energy conversion and long–lived excited states are needed to drive catalysis. With respect to these desirable qualities, a series of five 5,6–dicyano[2,1,3]benzothiadiazole (DCBT) dyes are examined as organic chromophores that can serve as strong photooxidants in catalytic systems. The series utilizes a DCBT core with aryl groups on the periphery with varying electron donation strengths relative to the core. The dyes are studied via both steady–state and transient absorption and emission studies. Additionally, computational analysis, voltammetry, crystallography, and absorption spectroelectrochemistry are also used to better understand the behavior of these dyes. Ultimately, a strong photooxidant is arrived at with an exceptionally long excited state lifetime for an organic chromophore of 16 µs. Finally, the long–lived excited state photosensitizer is well–suited for use in catalysis, and visible light driven photosensitized water oxidation is demonstrated using a water–soluble photosensitizer.

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

A dynamic kinetic model captures cell-free metabolism for improved butanol production

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 hours. Here, we present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

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

Navier: Dataflow Architecture for Computation Chemistry

Navier’s objectives were two evaluate the use of emerging technologies, especially dataflow accelerators, for high-performance computing (HPC) applications, specifically in the domain of chemistry, and to develop a prototype software stack to support such applications. Navier builds on capabilities previously developed by synergistic projects, such as PNNL Data Model Convergence (DMC) LDRD Hardware Advanced Workflows (HAW) and DuOMO, as well as DOE ARIAA. Throughout its 18 months, the Navier team developed new capabilities and artifacts at all levels of the HW/SW stack, provided a seamless way to integrate novel computing architectures (Sambanova SN10 and Xilinx Versal AI) into an existing software stack, developed chemistry workflows, data analytics tools, and HPC molecular dynamics workflows that leverage the developed stack and PNNL institutional investments in emerging architectures. Navier also explored the use of active learning to accelerate a computational chemistry workflow for organic molecules on PNNL Junction cluster (in collaboration with AMD/Xilinx). Navier developed tools, methodologies, and studies for hardware software co-design and (sparse) dataflow accelerators that are composable and can be used together or separately. These methodologies are now used in other projects, such as DOE AMAIS and HPDA. This report describes Navier’s achievement, the developed tools and methodologies, and the research findings and conclusions.

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

Singlet fission for quantum information and quantum computing: the parallel JDE model

Abstract Singlet fission is a photoconversion process that generates a doubly excited, maximally spin entangled pair state. This state has applications to quantum information and computing that are only beginning to be realized. In this article, we construct and analyze a spin-exciton hamiltonian to describe the dynamics of the two-triplet state. We find the selection rules that connect the doubly excited, spin-singlet state to the manifold of quintet states and comment on the mechanism and conditions for the transition into formally independent triplets. For adjacent dimers that are oriented and immobilized in an inert host, singlet fission can be strongly state-selective. We make predictions for electron paramagnetic resonance experiments and analyze experimental data from recent literature. Our results give conditions for which magnetic resonance pulses can drive transitions between optically polarized magnetic sublevels of the two-exciton states, making it possible to realize quantum gates at room temperature in these systems.

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

Improving Estimation of the Koopman Operator with Kolmogorov–Smirnov Indicator Functions

It has become common to perform kinetic analysis using approximate Koopman operators that transform high-dimensional timeseries of observables into ranked dynamical modes. The key to the practical success of the approach is the identification of a set of observables that form a good basis on which to expand the slow relaxation modes. Good observables are, however, difficult to identify a priori and suboptimal choices can lead to significant underestimations of characteristic time scales. Leveraging the representation of slow dynamics in terms of Hidden Markov Models (HMM), we propose a simple and computationally efficient clustering procedure to infer surrogate observables that form a good basis for slow modes. Here, we apply the approach to an analytically solvable model system as well as on three protein systems of different complexities. We consistently demonstrate that the inferred indicator functions can significantly improve the estimation of the leading eigenvalues of Koopman operators and correctly identify key states and transition time scales of stochastic systems, even when good observables are not known a priori.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Projector-Based Quantum Embedding for Molecular Systems: An Investigation of Three Partitioning Approaches

Projector-based embedding is a relatively recent addition to the collection of methods that seek to utilize chemical locality to provide improved computational efficiency. This work considers the interactions between the different proposed procedures for this method and their effects on the accuracy of the results. The interplay between the embedded background, projector type, partitioning scheme, and level of atomic orbital (AO) truncation are investigated on a selection of reactions from the literature. The Huzinaga projection approach proves to be more reliable than the level-shift projection when paired with other procedural options. Active subsystem partitioning from the subsystem projected AO decomposition (SPADE) procedure proves slightly better than the combination of Pipek-Mezey localization and Mulliken population screening (PMM). Along with these two options, a new partitioning criteria is proposed based on subsystem von Neumann entropy and the related subsystem orbital occupancy. This new method overlaps with the previous PMM method, but the screening process is computationally simpler. Finally, AO truncation proves to be a robust option for the tested systems when paired with the Huzinaga projection, with satisfactory results being acquired at even the most severe truncation level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Solid-Binding Proteins: Bridging Synthesis, Assembly, and Function in Hybrid and Hierarchical Materials Fabrication

There is considerable interest in the development of hybrid organic–inorganic materials because of the potential for harvesting the unique capabilities that each system has to offer. Proteins are an especially attractive organic component owing to the high amount of chemical information encoded in their amino acid sequence, their amenability to molecular and computational (re)design, and the many structures and functions they specify. Genetic installation of solid-binding peptides (SBPs) within protein frameworks affords control over the position and orientation of adhesive and morphogenetic segments, and a path toward predictive synthesis and assembly of functional materials and devices, all while harnessing the built-in properties of the host scaffold. Furthermore, we review the current understanding of the mechanisms through which SBPs bind to technologically relevant interfaces, with an emphasis on the variables that influence the process, and highlight the last decade of progress in the use of solid-binding proteins for hybrid and hierarchical materials synthesis.

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

Strategies for simultaneous strengthening and toughening via nanoscopic intracrystalline defects in a biogenic ceramic

While many organisms synthesize robust skeletal composites consisting of spatially discrete organic and mineral (ceramic) phases, the intrinsic mechanical properties of the mineral phases are poorly understood. Using the shell of the marine bivalve Atrina rigida as a model system, and through a combination of multiscale structural and mechanical characterization in conjunction with theoretical and computational modeling, we uncover the underlying mechanical roles of a ubiquitous structural motif in biogenic calcite, their nanoscopic intracrystalline defects. These nanoscopic defects not only suppress the soft yielding of pure calcite through the classical precipitation strengthening mechanism, but also enhance energy dissipation through controlled nano- and micro-fracture, where the defects’ size, geometry, orientation, and distribution facilitate and guide crack initialization and propagation. These nano- and micro-scale cracks are further confined by larger scale intercrystalline organic interfaces, enabling further improved damage tolerance.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Simulating water adsorption in metal–organic frameworks with open metal sites using the 12-6-4 Lennard–Jones potential

Metal-organic frameworks (MOFs) with coordinatively unsaturated open metal sites (OMSs) are promising sorbent materials in atmospheric water harvesting (AWH) systems at low relative humidity (RH) due to their strong interactions with water molecules. However, accurate computational modelling of water adsorption in those materials are challenging, as standard force fields (FFs) based on the 12-6 Lennard-Jones (L-J) potential cannot properly describe the water-OMS interactions. In biomolecular simulations, the 12-6-4 L-J potential has been successfully used to model metal ion-water interactions in which the extra 1/r 4 -term is for charge-induced dipole electrostatics. In this work, we adopted this strategy and used the 12-6-4 L-J potential to model water-OMS interactions in MOFs for low RH AWH applications. Notably, without any modifications, the parameters used for aqueous metal ions were able to greatly improve the accuracy in predicting water adsorption isotherms in MOF-74, which highlights the simplicity and transferability of this method.

74 ATOMIC AND MOLECULAR PHYSICS↗