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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

CAS without SCF—Why to use CASCI and where to get the orbitals

The complete active space self-consistent field (CASSCF) method has seen broad adoption due to its ability to describe the electronic structure of both the ground and excited states of molecules over a broader swath of the potential energy surface than is possible with the simpler Hartree–Fock approximation. However, it also has a reputation for being unwieldy, computationally costly, and un-black-box. Here, we discuss a class of alternatives, complete active space configuration interaction (CASCI) methods, paying particular attention to their application to electronic excited states. The goal of this Perspective is fourfold. First, we argue that CASCI is not merely an approximation to CASSCF, in that it can be designed to have important qualitative advantages over CASSCF. Second, we present several insights drawn from our experience experimenting with different schemes for computing orbitals to be employed in CASCI. Third, we argue that CASCI is well suited for application to nanomaterials. Lastly, we reason that, with the rise in new low-scaling approaches for describing multireference systems, there is a greater need than ever to develop new methods for defining orbitals that provide an efficient and accurate description of both static correlation and electronic excitations in a limited active space.

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

GPU acceleration of rank-reduced coupled-cluster singles and doubles

Here, we have developed a graphical processing unit (GPU) accelerated implementation of our recently introduced rank-reduced coupled-cluster singles and doubles (RR-CCSD) method. RR-CCSD introduces a low-rank approximation of the doubles amplitudes. This is combined with a low-rank approximation of the electron repulsion integrals via Cholesky decomposition. The result of these two low-rank approximations is the replacement of the usual fourth-order CCSD tensors with products of second- and third-order tensors. In our implementation, only a single fourth-order tensor must be constructed as an intermediate during the solution of the amplitude equations. Owing in large part to the compression of the doubles amplitudes, the GPU-accelerated implementation shows excellent parallel efficiency (95% on eight GPUs). Our implementation can solve the RR-CCSD equations for up to 400 electrons and 1550 basis functions—roughly 50% larger than the largest canonical CCSD computations that have been performed on any hardware. In addition to increased scalability, the RR-CCSD computations are faster than the corresponding CCSD computations for all but the smallest molecules. We test the accuracy of RR-CCSD for a variety of chemical systems including up to 1000 basis functions and determine that accuracy to better than 0.1% error in the correlation energy can be achieved with roughly 95% compression of the ov space for the largest systems considered. We also demonstrate that conformational energies can be predicted to be within 0.1 kcal mol -1 with efficient compression applied to the wavefunction. Finally, we find that low-rank approximations of the CCSD doubles amplitudes used in the similarity transformation of the Hamiltonian prior to a conventional equation-of-motion CCSD computation will not introduce significant errors (on the order of a few hundredths of an electronvolt) into the resulting excitation energies.

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

In situ temperature measurements in sooting methane/air flames using synchrotron x-ray fluorescence of seeded krypton atoms

Synchrotron x-ray fluorescence has been used to measure temperatures in optically dense gases where traditional methods would fail. These data provide a benchmark for stringent tests of computational fluid dynamics models for complex systems where physical and chemical processes are intimately linked. The experiments measured krypton number densities in a sooting, atmospheric pressure, nonpremixed coflow flame that is widely used in combustion research. The experiments not only form targets for the models, but the simulations also identify potential sources of uncertainties in the measurements, allowing for future improvements.

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

A System Engineering Approach in the Analysis of Ionic Liquids Properties [Thesis]

Room temperature ionic liquids (RTIL) are ionic compounds, comprised of a cation and anion, in the liquid state at temperatures below 100 ⁰C. There are estimated 10 18 anion and cation combinations, which can create RTILs with unique properties. While the ionic character of RTILs made them attractive in a wide variety of electrochemical applications, their behaviors under electrochemical conditions are not well understood. In this research, we developed a RTIL testbed to investigate how RTILs differ from molecular solvents and electrolytes in electrochemical systems through the study of the effects of their morphology and chemical properties on the electron transfer processes. The test bed is composed of three modules: Electrochemistry, Material characterization, Computational. All modules have been completely developed, verified, and integrated and two of them, i.e., Electrochemistry and Material characterization have been validated in this dissertation. To evaluate the proposed system, experiments were performed on solutions of a redox active species in RTILs. It was found that it is possible to investigate the RTIL's morphology and chemical properties' impact on the electron transfer processes of the solute based on the outcomes and evaluations of tests performed by the RTIL testbed.

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

Root system architecture in cereals: progress, challenges and perspective

We report roots are essential multifunctional plant organs involved in water and nutrient uptake, metabolite storage, anchorage, mechanical support, and interaction with the soil environment. Understanding of this ‘hidden half’ provides potential for manipulation of root system architecture (RSA) traits to optimize resource use efficiency and grain yield in cereal crops. Unfortunately, root traits are highly neglected in breeding due to the challenges of phenotyping, but could have large rewards if the variability in RSA traits can be fully exploited. Until now, a plethora of genes have been characterized in detail for their potential role in improving RSA. The use of forward genetics approaches to find sequence variations in genes underpinning desirable RSA would be highly beneficial. Advances in computer vision applications have allowed image-based approaches for high-throughput phenotyping of RSA traits that can be used by any laboratory worldwide to make progress in understanding root function and dissection of the genetics. At the same time, the frontiers of root measurement include non-invasive methods like X-ray computer tomography and magnetic resonance imaging that facilitate new types of temporal studies. Root physiology and ecology are further supported by spatiotemporal root simulation modeling. The discovery of component traits providing improved resilience and yield advantage in target environments is a key necessity for mainstreaming root-based cereal breeding. The integrated use of pan-genome resources, now available in most cereals, coupled with new in-field phenotyping platforms has the potential for precise selection of superior genotypes with improved RSA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Optimization of Conditions for Photoluminescence-Based Sensing of Critical Minerals: Strategies and Outlook

Economically critical minerals and metals are vital to America’s security, with application areas in energy, advanced technologies, and defense systems. The growing implementation of renewable energy sources such as electric vehicles is expected to significantly increase demand for these minerals, while the global supply is monopolistic, with most of the production occurring in a few countries outside of the US. To mitigate potential supply chain vulnerabilities, the domestic production of economically critical metals has become a bipartisan priority of the US government, and unconventional sources such as coal and its utilization byproducts such as fly ash and acid mine drainage are being actively explored as an alternative method for meeting domestic demand. Slow and expensive characterization methods for critical metals present a significant pain point for domestic production, which has led to the exploration of alternative low-cost, portable platforms capable of detecting target metals during resource prospecting and downstream process monitoring. The National Energy Technology Laboratory has developed a portable fiber optic-based luminescence spectrometer that utilizes a metal-organic framework (MOF) material to detect and distinguish parts-per-billion levels of the visible-emitting rare earth elements terbium, dysprosium, samarium, and europium, which are all economically critical metals. Although preliminary results with this system are promising, a crucial barrier to practical deployment is the ability to operate in low pH, high ionic strength environments, as acidic conditions and the presence of other metal ions can significantly reduce luminescence signal. Here, current strategies related to sample treatment, material design, and sensor configuration are discussed in the context of improving sensor performance in application-relevant conditions. Additionally, future opportunities, such as the optimization of the sensing binding environment through computational methods and specific metals to be removed for treatment will be discussed, presenting a forward-looking view for innovation in luminescent sensing of critical metals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bridging Aromatic/Antiaromatic Units: Recent Advances in Aromaticity and Antiaromaticity in Main-group and Transition-Metal Clusters from Bonding and Magnetic Analyses

Synthetic exploration and theoretical characterization of metal clusters are actively developing branches of modern inorganic chemistry. Advances in these areas constantly expand the rich structural diversity of viable species, allowing a detailed study of the fundamental characteristics of bench-stable compounds. In this minireview, we summarize recent achievements in synthesis and computational analyses of main-group and heterometallic clusters containing multiple aromatic/antiaromatic units. These systems range from bare clusters to ligand-decorated aggregates, providing a fundamental understanding of the aromaticity and antiaromaticity concepts in species exhibiting unprecedented shapes and composition. The review gives a comprehensive summary of bonding and magnetic response properties of such systems deciphered from the Adaptive Natural Density Partitioning (AdNDP) approach and induced magnetic field analyses.

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

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

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

Reshaping the 2‐Pyrone Synthase Active Site for Chemoselective Biosynthesis of Polyketides

Abstract Engineering enzymes with novel reactivity and applying them in metabolic pathways to produce valuable products are quite challenging due to the intrinsic complexity of metabolic networks and the need for high in vivo catalytic efficiency. Triacetic acid lactone (TAL), naturally generated by 2‐pyrone synthase (2PS), is a platform molecule that can be produced via microbial fermentation and further converted into value‐added products. However, these conversions require extra synthetic steps under harsh conditions. We herein report a biocatalytic system for direct generation of TAL derivatives under mild conditions with controlled chemoselectivity by rationally engineering the 2PS active site and then rewiring the biocatalytic pathway in the metabolic network of E. coli to produce high‐value products, such as kavalactone precursors, with yields up to 17 mg/L culture. Computer modeling indicates sterics and hydrogen‐bond interactions play key roles in tuning the selectivity, efficiency and yield.

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

Recent Advances for Improving the Accuracy, Transferability, and Efficiency of Reactive Force Fields

Reactive force fields provide an affordable model for simulating chemical reactions at a fraction of the cost of quantum mechanical approaches. However, classically accounting for chemical reactivity often comes at the expense of accuracy and transferability, while computational cost is still large relative to nonreactive force fields. Here, we summarize recent efforts for improving the performance of reactive force fields in these three areas with a focus on the ReaxFF theoretical model. To improve accuracy, we describe recent reformulations of charge equilibration schemes to overcome unphysical long-range charge transfer, new ReaxFF models that account for explicit electrons, and corrections for energy conservation issues of the ReaxFF model. To enhance transferability we also highlight new advances to include explicit treatment of electrons in the ReaxFF and hybrid nonreactive/reactive simulations that make it possible to model charge transfer, redox chemistry, and large systems such as reverse micelles within the framework of a reactive force field. To address the computational cost, we review recent work in extended Lagrangian schemes and matrix preconditioners for accelerating the charge equilibration method component of ReaxFF and improvements in its software performance in LAMMPS.

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

Photogenerated Charge Separation in a CdSe Nanocluster Encapsulated in a Metal–Organic Framework for Improved Photocatalysis

Nanoclusters encapsulated in a metal–organic framework (MOF) are the subject of intense interest because of their high promise for photocatalysis. We show computationally that a CdSe nanocluster incorporated into NU-1000 (Cd 6 Se 6 @NU-1000) allows electron transfer from a visible-light photo-excited organic linker to the lowest unoccupied crystal orbital of the inorganic cluster, which can lead to charge separation. Substituents on the linker can be used to tune the band position and the band gap of the whole composite (Cd 6 Se 6 @NU-1000), and this makes it possible to perform the water splitting reaction under visible light. Here, the achievement of charge separation within the MOF leads to longer life of the excited states, and as a result, it can enhance the photocatalytic activity of the system.

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

Distinct vibrational motions promote disparate excited-state decay pathways in cofacial perylenediimide dimers

A complex interplay of structural, electronic, and vibrational degrees of freedom underpins the fate of molecular excited states. Organic assemblies exhibit a myriad of excited-state decay processes, such as symmetry-breaking charge separation (SB-CS), excimer (EX) formation, singlet fission, and energy transfer. Recent studies of cofacial and slip-stacked perylene-3,4:9,10-bis(dicarboximide) (PDI) multimers demonstrate that slight variations in core substituents and H- or J-type aggregation can determine whether the system follows an SB-CS pathway or an EX one. However, questions regarding the relative importance of structural properties and molecular vibrations in driving the excited-state dynamics remain. Here, we use a combination of two-dimensional electronic spectroscopy, femtosecond stimulated Raman spectroscopy, and quantum chemistry computations to compare the photophysics of two PDI dimers. The dimer with 1,7-bis(pyrrolidin-1′-yl) substituents (5PDI2) undergoes ultrafast SB-CS from a photoexcited mixed state, while the dimer with bis-1,7-(3′,5′-di-t-butylphenoxy) substituents (PPDI2) rapidly forms an EX state. Examination of their quantum beating features reveals that SB-CS in 5PDI2 is driven by the collective vibronic coupling of two or more excited-state vibrations. In contrast, we observe signatures of low-frequency vibrational coherence transfer during EX formation by PPDI2, which aligns with several previous studies. We conclude that key electronic and structural differences between 5PDI2 and PPDI2 determine their markedly different photophysics.

Chemistry↗
DOE OSTI2022

CFD Simulation of Biomass Pyrolysis Vapor Upgrading over a Pt/TiO 2 Catalyst in Fixed and Moving Beds

This report presents a comprehensive computational fluid dynamics (CFD) study of the biomass pyrolysis vapor phase upgrading and the catalyst regeneration in different reactors under various operational conditions. This study used the open-source software Multiphase Flow with Interphase eXchanges (MFiX), developed at National Energy Technology Laboratory (NETL) for simulating hydrodynamics, heat transfer, and chemical reactions in multiphase systems. This simulation solved the conservation of mass, momentum, energy, species for gas and solid phases with chemical reactions.

09 BIOMASS FUELS↗
DOE OSTI2026

Data and scripts associated with a manuscript modeling microbial regulation of priming effects

This data package is associated with the publication “Modeling Microbial Regulatory Feedback in Organic Matter Decomposition Identifies Copiotrophic Traits as Key Drivers of Positive Priming” published as a preprint on BioRXiv by Ahamed et al. (2026); https://doi.org/10.1101/2024.08.11.607483. The package contains MATLAB scripts and saved simulation outputs used to implement a cybernetic model of microbial regulation during complex organic matter (OM) decomposition governing priming effects. It includes models of (i) single microbial functional groups (copiotrophic or oligotrophic degraders) and (ii) binary consortia composed of degraders and non-degraders with contrasting or common growth traits. Simulation results were generated using Monte Carlo analyses, with randomized key model parameters across a range of environmental mixing fractions of complex and labile OM. The dataset was created to provide a transparent and reusable computational framework for systematically exploring how microbial growth traits, metabolic regulation, and community composition influence OM decomposition dynamics and priming effects. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes the variable definitions. This package includes: (1) annotated MATLAB code implementing the system of ordinary differential equations and cybernetic control laws; (2) saved output files containing data (e.g., biomass, substrates, enzyme levels, priming metrics); and (3) scripts for processing saved outputs and regenerating figures. Specifically, the data package contains three main MATLAB scripts: runPrimingModel.m, runPlotData.m, and runPlotSuppFigS1.m, along with this readme and supporting documentation. Users should begin with runPrimingModel.m, which contains the annotated code implementing the system of ordinary differential equations and cybernetic control laws. This script runs the Monte Carlo simulations of microbial OM decomposition and allows users to modify microbial trait definitions, adjust parameter distributions, or define new community configurations. Simulation outputs are automatically saved as .mat files in the folder named SavedData, which stores all pre-generated results included in this package. The second script, runPlotData.m, reads files from the SavedData folder and processes them to regenerate the figures presented in the manuscript. The third script, runPlotSuppFigS1.m, specifically generates Figure S1 in the Supplementary Material of the manuscript. The package also includes the aforementioned files in non-proprietary .txt format. If users intend to use them, they should first save the files in their respective .m or .mat formats prior to execution in MATLAB.

Biomass concentration↗
DOE OSTI2021

Nanoparticle O–H Bond Dissociation Free Energies from Equilibrium Measurements of Cerium Oxide Colloids

A novel equilibrium strategy for measuring the hydrogen atom affinity of colloidal metal oxide nanoparticles is presented. Reactions between oleate-capped cerium oxide nanoparticle colloids (nanoceria) and organic proton-coupled electron transfer (PCET) reagents are used as a model system. Nanoceria redox changes, or hydrogen loadings, and overall reaction stoichiometries were followed by both 1 H NMR and X-ray absorption near-edge spectroscopies. Furthermore, these investigations revealed that, in many cases, reactions between nanoceria and PCET reagents reach equilibrium states with good mass balance. Each equilibrium state is a direct measure of the bond strength, or bond dissociation free energy (BDFE), between nanoceria and hydrogen. Further studies, including those with larger nanoceria, indicated that the relevant bond is a surface O–H. Thus, we have measured surface O–H BDFEs for nanoceria—the first experimental BDFEs for any nanoscale metal oxide. Remarkably, the measured CeO–H BDFEs span 13 kcal mol –1 (0.56 eV) with changes in the average redox state of the nanoceria colloid. Possible chemical models for this strong dependence are discussed. We propose that the tunability of ceria BDFEs may be important in explaining its effectiveness in catalysis. More generally, metal oxide BDFEs have been used as predictors of catalyst efficacy that, traditionally, have only been accessible by computational methods. These results provide important experimental benchmarks for metal oxide BDFEs and demonstrate that the concepts of molecular bond strength thermochemistry can be applied to nanoscale materials.

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

Inverse methods for design of soft materials

Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide a systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. Furthermore, while multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Constructing tensor network influence functionals for general quantum dynamics

Here, we describe an iterative formalism to compute influence functionals that describe the general quantum dynamics of a subsystem beyond the assumption of linear coupling to a quadratic bath. We use a space-time tensor network representation of the influence functional and investigate its approximability in terms of its bond dimension and time-like entanglement in the tensor network description. We study two numerical models, the spin-boson model and a model of interacting hard-core bosons in a 1D harmonic trap. We find that the influence functional and the intermediates involved in its construction can be efficiently approximated by low bond dimension tensor networks in certain dynamical regimes, which allows the quantum dynamics to be accurately computed for longer times than with direct time evolution methods. However, as one iteratively integrates out the bath, the correlations in the influence functional can first increase before decreasing, indicating that the final compressibility of the influence functional is achieved via non-trivial cancellation.

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

Application of Quantum Computing to Biochemical Systems: A Look to the Future

Chemistry is considered as one of the more promising applications to science of near-term quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Because of the limitations of near-term quantum computers, the most effective strategies split the work over classical and quantum computers. There is a proven set of methods in computational chemistry and materials physics that has used this same idea of splitting a complex physical system into parts that are treated at different levels of theory to obtain solutions for the complete physical system for which a brute force solution with a single method is not feasible. These methods are variously known as embedding, multi-scale, and fragment techniques and methods. We review these methods and then propose the embedding approach as a method for describing complex biochemical systems, with the parts not only treated with different levels of theory, but computed with hybrid classical and quantum algorithms. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with traditional algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗