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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

High-Current Density Durability of Pt/C and PtCo/C Catalysts at Similar Particle Sizes in PEMFCs

The durability of carbon supported PtCo-alloy based nanoparticle catalysts play a key role in the longevity of proton-exchange membrane fuel cells (PEMFC) in electric vehicle applications. To improve its durability, it is important to understand and mitigate the various factors that cause PtCo-based cathode catalyst layers (CCL) to lose performance over time. These factors include i) electrochemical surface area (ECSA) loss, ii) specific activity loss, iii) H + /O 2 -transport changes and iv) Co 2+ contamination effects. We use a catalyst-specific accelerated stress test (AST) voltage cycling protocol to compare the durability of Pt and PtCo catalysts at similar average nanoparticle size and distribution. Our studies indicate that while Pt and PtCo nanoparticle catalysts suffer from similar magnitudes of electrochemical surface area (ECSA) losses, PtCo catalyst shows a significantly larger cell voltage loss at high current densities upon durability testing. The distinctive factor causing the large cell voltage loss of PtCo catalyst appears to be the secondary effects of the leached Co 2+ cations that contaminate the electrode ionomer. A 1D performance model has been used to quantify the cell voltage losses arising from various factors causing degradation of the membrane electrode assembly (MEA).

08 HYDROGEN↗
DOE OSTI2021

Comparison of the Forward and Reverse Photocycle Dynamics of Two Highly Similar Canonical Red/Green Cyanobacteriochromes Reveals Unexpected Differences

Cyanobacteriochromes (CBCRs) are cyanobacterial photoreceptors that exhibit photochromism between two states: a thermally stable dark-adapted state and a metastable light-adapted state with bound linear tetrapyrrole (bilin) chromophores possessing 15Z and 15E configurations, respectively. The photodynamics of canonical red/green CBCRs have been extensively studied; however, the time scales of their excited-state lifetimes and subsequent ground-state evolution rates widely differ and, at present, remain difficult to predict. Here, we compare the photodynamics of two closely related red/green CBCRs that have substantial sequence identity (~68%) and similar chromophore environments: AnPixJg2 from Anabaena sp. PCC 7120 and NpR6012g4 from Nostoc punctiforme. Using broadband transient absorption spectroscopy on the primary (125 fs to 7 ns) and secondary (7 ns to 10 ms) time scales together with global analysis modeling, our studies revealed that AnPixJg2 and NpR6012g4 have comparable quantum yields for initiating the forward ( 15Z P r → 15E P g ) and reverse ( 15E P g → 15Z P r ) reactions, which proceed through monotonic and nonmonotonic mechanisms, respectively. In addition to small discrepancies in the kinetics, the secondary reverse dynamics resolved unique features for each domain: intermediate shunts in NpR6012g4 and a Meta-Gf intermediate red-shifted from the 15Z P r photoproduct in AnPixJg2. Altogether, this study supports the conclusion that sequence similarity is a useful criterion for predicting pathways of the light-induced evolution and quantum yield of generating primary intermediate Φ p within subfamilies of CBCRs, but more studies are still needed to develop a comprehensive molecular level understanding of these processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Permutable SOS (symmetry operational similarity)

Abstract Based on symmetry consideration, quasi-one-dimensional (1D) objects, relevant to numerous observables or phenomena, can be classified into eight different types. We provide various examples of each 1D type and discuss their symmetry operational similarity (SOS) relationships, which are often permutable. A number of recent experimental observations, including current-induced magnetization in polar or chiral conductors, non-linear Hall effect in polar conductors, spin-polarization of tunneling current to chiral conductors, and ferro-rotational domain imaging with linear gyration are discussed in terms of (permutable) SOS. In addition, based on (permutable) SOS, we predict a large number of new phenomena in low symmetry materials that can be experimentally verified in the future.

Materials Science↗
DOE OSTI2020

Distinct assembly mechanisms underlie similar biogeographical patterns of rare and abundant bacteria in Tibetan Plateau grassland soils

Rare biosphere represents the majority of Earth's biodiversity and performs vital ecological functions, yet little is known about its biogeographical patterns and community assembly processes in terrestrial ecosystems. Herein, we investigated the community composition and phylogeny of rare (relative abundance <0.1%) and abundant (>1%) bacteria in dryland grassland soils on the Tibetan Plateau. Results revealed similar biogeographical patterns of rare and abundant bacteria at both compositional and phylogenetic levels, but rare subcommunity was more heavily influenced by stochasticity (72%) than the abundant (57%). The compositional variation of rare bacteria was less explained by environmental factors (41%) than that of the abundant (80%), while the phylogeny of rare bacteria (36%) was more explained than that of the abundant (29%). The phylogeny of rare bacteria was equally explained by local factors (soil and vegetation) and geospatial distance (11.5% and 11.9% respectively), while that of the abundant was more explained by geospatial distance (22.1%) than local factors (11.3%). Furthermore, a substantially tighter connection between the community phylogeny and composition was observed in rare (R(2) = 0.65) than in abundant bacteria (R(2) = 0.08). Our study provides novel insights into the assembly processes and biographical patterns of rare and abundant bacteria in dryland soils.

Ji, Mukan↗
DOE OSTI2021

Single-crystalline epitaxial TiO film: A metal and superconductor, similar to Ti metal

Titanium monoxide (TiO), an important member of the rock salt 3d transition-metal monoxides, has not been studied in the stoichiometric single-crystal form. It has been challenging to prepare stoichiometric TiO due to the highly reactive Ti 2+ . We adapt a closely lattice-matched MgO(001) substrate and report the successful growth of single-crystalline TiO(001) film using molecular beam epitaxy. This enables a first-time study of stoichiometric TiO thin films, showing that TiO is metal but in proximity to Mott insulating state. We observe a transition to the superconducting phase below 0.5 K close to that of Ti metal. Density functional theory (DFT) and a DFT-based tight-binding model demonstrate the extreme importance of direct Ti–Ti bonding in TiO, suggesting that similar superconductivity exists in TiO and Ti metal. Our work introduces the new concept that TiO behaves more similar to its metal counterpart, distinguishing it from other 3d transition-metal monoxides.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Wild bees and natural enemies prefer similar flower species and respond to similar plant traits

Designing wildflower habitats to support beneficial insects providing pollination and pest control services is important for supporting sustainable crop production. It is often desirable to support both groups of insects, making the selection of resource plants for insect conservation programs more challenging. Moreover, the process of selecting resource plants is complicated by the array of possible options in each region, and the need to provide resources over the entire growing season. Identifying traits shared by resource plants that are attractive to both bees and natural enemies can reduce the need to evaluate new plants in each region, by providing a guide for the types of plants expected to be rewarding to these insects. Using insect visitation data collected from replicated common garden plantings of native wildflower and shrub species from the Great Lakes region of the United States, we found a high degree of correlation between the abundance of bees and natural enemies visiting native plant species. These results were used to identify a set of 15 plant species that can provide resources for these insects throughout the summer. Across all tested species, pollen quantity per flower and the week of bloom were positively correlated with some, but not all, taxonomic groupings of beneficial insects. In contrast, floral area was consistently positively associated with visitation of both natural enemies and wild bees. This trait is easy to document and can allow for efficient local testing of potential resource plants, providing a faster path to implementing insect conservation in working landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Power of Many: An Ensemble Approach to Spectral Similarity

Quantifying the similarity between two mass spectra─a known reference mass spectrum and an unidentified sample mass spectrum─is at the heart of compound identification workflows in gas chromatography–mass spectrometry (GC-MS). The reference spectrum most like the sample is assigned as its identification (provided some quantitative similarity threshold is met, e.g., 80%) and thus accurately measuring similarity is essential. Significant research has gone toward developing metrics for this purpose, each of which has attempted to improve upon existing methods by incorporating GC-MS-specific information (e.g., peak ratios or retention times) or adopting various statistical and algorithmic frameworks. While this active development has led to a plethora of similarity metrics with demonstrated value across different contexts, the unfortunate consequence has been confusion surrounding which metric should be used as a global standard. No such metric is currently accepted as the standard method because different metrics have demonstrated optimal performance in different contexts. In this work, we propose an ensemble approach to spectral similarity scoring that combines the collective information from across existing similarity metrics to form an improved, globally representative similarity metric as a step toward establishing a global standard method. In conclusion, the resulting ensemble metrics are evaluated on over 88,000 spectra of varying complexity and demonstrate improved abilities to accurately rank the correct reference spectrum as the top-matching candidate for a sample relative to the rankings generated by individual similarity scores.

Carbohydrates↗
DOE OSTI2021

NMR Spectroscopy for Protein Higher Order Structure Similarity Assessment in Formulated Drug Products

Peptide and protein drug molecules fold into higher order structures (HOS) in formulation and these folded structures are often critical for drug efficacy and safety. Generic or biosimilar drug products (DPs) need to show similar HOS to the reference product. The solution NMR spectroscopy is a non-invasive, chemically and structurally specific analytical method that is ideal for characterizing protein therapeutics in formulation. However, only limited NMR studies have been performed directly on marketed DPs and questions remain on how to quantitively define similarity. Here, NMR spectra were collected on marketed peptide and protein DPs, including calcitonin-salmon, liraglutide, teriparatide, exenatide, insulin glargine and rituximab. The 1D 1 H spectral pattern readily revealed protein HOS heterogeneity, exchange and oligomerization in the different formulations. Principal component analysis (PCA) applied to two rituximab DPs showed consistent results with the previously demonstrated similarity metrics of Mahalanobis distance (D M ) of 3.3. The 2D 1 H- 13 C HSQC spectral comparison of insulin glargine DPs provided similarity metrics for chemical shift difference (Δδ) and methyl peak profile, i.e., 4 ppb for 1 H, 15 ppb for 13 C and 98% peaks with equivalent peak height. Finally, 2D 1 H- 15 N sofast HMQC was demonstrated as a sensitive method for comparison of small protein HOS. The application of NMR procedures and chemometric analysis on therapeutic proteins offer quantitative similarity assessments of DPs with practically achievable similarity metrics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Neural architecture search via similarity adaptive guidance

Evolutionary neural network architecture search (ENAS) has attracted the attention of many experts due to its global optimization capabilities to automatically search for convolutional neural network architectures based on the target task. The current search space for ENAS is not to design a fully structured network, but to search for smaller cell architectures to reduce search costs. However, blind search strategies do not effectively utilize the potential experience of the population. In order to utilize the potential experience learned by the current population to guide the evolutionary search of the population, we propose a similarity guided neural network architecture search algorithm based on cell architecture, which utilizes the similarity between pairwise architectures in the population as empirical knowledge learned by the population. Our proposed algorithm provides a novel method for calculating architecture similarity, which calculates architecture similarity separately from the cell and macro-structure. Then we decouple the connections and operations in the cell and calculate connection and operation similarity separately. In addition, we propose adaptive similarity selection and binary tournament selection strategies to enhance the algorithm’s global and local search capabilities and effectively explore the search space. Finally, we design an improved single-point crossover operator to enhance the local search ability of the evolutionary operator. The experimental results show that SAGNAS is a competitive algorithm that achieves 97.44% and 81.60% in CIFAR10 and CIFAR100 with only 1.9 GPU-days spent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Generalizing Similarity Laws for Radio-Frequency Discharge Plasmas across Nonlinear Transition Regimes

We generalize similarity theory based on the scaling and solution invariance of the Boltzmann equation, coupled with the Poisson equation, and demonstrate similarity laws for radio-frequency (rf) discharge plasmas across three nonlinear transitional regimes, namely, the alpha-gamma mode transition, the stochastic-Ohmic-heating mode transition, and the bounce-resonance-heating mode transition. Fundamental plasma parameters, e.g., the electron power absorption, under similar discharge conditions are examined via fully kinetic particle-in-cell simulations, and electron-kinetic invariance is exemplified in similar rf discharge plasmas. The results unambiguously confirm the applicability of similarity laws for rf plasmas in extended operating regimes, and strengthen the foundations and framework of similarity physics with universality.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search (Special Session Paper)

Molecular similarity search has been widely used in drug discovery to rapidly identify structurally similar compounds from large molecular databases. With the increasing size of chemical libraries, there is growing interest in the efficient ac- celeration of large-scale similarity search. Existing works mainly focus on CPU and GPU to accelerate the computation of Tatimoto coefficient in measuring the pairwise similarity between different molecular fingerprints. In this paper, we propose and optimize an FPGA-based accelerator design on exhaustive and approximate search algorithms. On exhaustive search using BitBound & fold- ing, we analyze the similarity cutoff and folding level relationship with search speedup and accuracy, and propose a scalable on- the-fly query engine on FPGAs to reduce the resource utilization and pipeline interval. We achieve a 450 million compounds-per- second processing throughput for a single query engine. On approximate search using hierarchical navigable small world (HNSW), a popular algorithm with high recall and query speed, we propose an FPGA-based graph traversal engine to utilize high throughput register array based priority queue and fine- grained distance calculation engine to increase the processing capability. Experimental results show that the proposed FPGA- based HNSW implementation achieves a 35× speedup than existing works on CPU. To the best of our knowledge, our FPGA- based implementation is the first attempt to accelerate molecular similarity search on FPGA and has the highest performance among existing approaches.

Peng, Hongwu↗
DOE OSTI2023

Evidence of Mixed Scaling for Mean Profile Similarity in the Stable Atmospheric Surface Layer

A new mixed scaling parameter Z = z/(Lh)1/2 is proposed for similarity in the stable atmospheric surface layer, where z is the height, L is the Obukhov length, and h is the boundary layer depth. In comparison with the parameter ζ = z/L from Monin–Obukhov similarity theory (MOST), the new parameter Z leads to improved mean profile similarity for wind speed and air temperature in large-eddy simulations. It also yields the same linear similarity relation for CASES-99 field measurements, including in the strongly stable (but still turbulent) regime where large deviations from MOST are observed. Results further suggest that similarity for turbulent energy dissipation rate depends on both Z and ζ. The proposed mixed scaling of Z and relevance of h can be explained by physical arguments related to the limit of z-less stratification that is reached asymptotically above the surface layer. The presented evidence and fitted similarity relations are promising, but the results and arguments are limited to a small sample of idealized stationary stable boundary layers. Here, corroboration is needed from independent datasets and analyses, including for complex and transient conditions not tested here.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

AIMSim : An accessible cheminformatics platform for similarity operations on chemicals datasets

The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery applications to assess how new molecules differ from existing ones. Further, most tools target advanced users and lack general implementations accessible to the larger community. In this work, we introduce Artificial Intelligence Molecular Similarity (AIMSim), an accessible cheminformatics platform for performing similarity operations on collections of molecules called molecular datasets. AIMSim provides a unified platform to perform similarity-based tasks on molecular datasets, such as diversity quantification, outlier and novelty analysis, clustering, dimensionality reduction, and inter-molecular comparisons. AIMSim implements all major binary similarity metrics and molecular fingerprints and is provided as a Python package that includes support for command-line use as well as a Graphical User Interface for code-free utilization with fully interactive plots.

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

A biology-informed similarity metric for simulated patches of human cell membrane

Complex scientific inquiries rely increasingly upon large and autonomous multiscale simulation campaigns, which fundamentally require similarity metrics to quantify ‘sufficient’ changes among data and/or configurations. However, subject matter experts are often unable to articulate similarity precisely or in terms of well-formulated definitions, especially when new hypotheses are to be explored, making it challenging to design a meaningful metric. Furthermore, the key to practical usefulness of such metrics to enable autonomous simulations lies in in situ inference, which requires generalization to possibly substantial distributional shifts in unseen, future data. Here, we address these challenges in a cancer biology application and develop a meaningful similarity metric for ‘patches’—regions of simulated human cell membrane that express interactions between certain proteins of interest and relevant lipids. In the absence of well-defined conditions for similarity, we leverage several biology-informed notions about data and the underlying simulations to impose inductive biases on our metric learning framework, resulting in a suitable similarity metric that also generalizes well to significant distributional shifts encountered during the deployment. We combine these intuitions to organize the learned embedding space in a multiscale manner, which makes the metric robust to incomplete and even contradictory intuitions. Our approach delivers a metric that not only performs well on the conditions used for its development and other relevant criteria, but also learns key spatiotemporal relationships without ever being exposed to any such information during training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Impact of Thermal Scattering Law on Similarity Assessment in Light-Water or Polyethylene-Moderated Systems

A collaborative effort between Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) is underway to provide a technical basis and methodology for the criticality safety community to use the sum of fractions (SoF) method for generating limits for mixtures of “selected actinide nuclides” included in the ANSI/ANS 8.15 standard. The PNNL scope in this project is to define a range of mixtures of 233 U, 235 U, and 239 Pu moderated with either light water or polyethylene and to examine the critical masses for these mixtures. The ORNL scope is primarily to provide validation support for these studies. More complete discussion of the project and its validation aspects will be presented at the upcoming International Conference on Nuclear Criticality Safety (ICNC) this Fall in Sendai, Japan. Clear differences in benchmark similarity to application systems as assessed by the integral parameter ck are noted in the validation studies performed as part of this project as a function of moderator. The c k value is a correlation coefficient that represents that amount of shared uncertainty in k eff due to cross sections between two systems. Individual nuclide-reaction contributions between the two systems can be simply summed to arrive at the total c k value. Specifically, the c k values for light-water–moderated solution experiments are higher for a water-moderated application than for a polyethylene-moderated application. This result is neither totally unexpected nor surprising, but the magnitude of the difference was difficult to anticipate. The TSUNAMI sequence, in the SCALE 6.2.4 code package developed by ORNL, was used to generate eigenvalues and reactivity effects with perturbation-theory based approach through sensitivity coefficients for all nuclides in the system with all reactions and energy groups. The TSUNAMI-Indices and Parameters (IP) sequence then uses the sensitivity data generated through TSUNAMI to generate relational parameters (i.e., c k ) to determine the degree of similarity between systems. One detail of the SCALE material and data implementation must be discussed at this point. Several thermal scattering laws (TSLs) are available for 1 H. SCALE uses a different nuclide ID number for each TSL; essentially, each version of 1 H is treated as a unique nuclide. For example, 1 H bound in water ( 1 H-H2O) is assigned the nuclide ID 1001, whereas 1 H bound in polyethylene (h-poly) is assigned the nuclide ID 9001001. The same cross section data are used for all reactions in 1 H, regardless of TSL, except for scattering below the TSL cutoff energy. TSUNAMI-IP treats different nuclide IDs as different nuclides; thus, no uncertainty is shared between 1 H-H2O and h-poly, despite much of the same data, including covariance data, being used for both nuclides. This presents a question: how much of the difference in assessed similarity between water- and polyethylene-moderated systems is due to the moderators, and how much is caused by the treatment of 1 H-H2O and h poly with cross section and covariance data. The extended edits generated by TSUANMI-IP allow for an investigation of this issue specifically, as well as a demonstration of the general techniques available within TSUNAMI to understand the results of the similarity assessment. This paper presents and analyzes the similarity assessment of both water- and polyethylene-moderated systems for a single benchmark: PST-002-001.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15

Abstract Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter‐chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and performed very well in estimating the global structure accuracy of assembly models. The average per‐target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per‐target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analyzed. The results demonstrate that combining the multi‐model method (PSS) with the complementary single‐model method (ICPS) is a promising approach to EMA.

59 BASIC BIOLOGICAL SCIENCES↗