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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Challenges of Modeling Defect Behavior and Plasticity across Spatial and Temporal Scales: A Case Study of Metal Bilayer Impact

Atomistic molecular dynamics (MD) and a microstructural dislocation density-based crystalline plasticity (DCP) framework were used together across time scales varying from picoseconds to nanoseconds and length scales spanning from angstroms to micrometers to model a buried copper–nickel interface subjected to high strain rates. The nucleation and evolution of defects, such as dislocations and stacking faults, as well as large inelastic strain accumulations and wave-induced stress reflections were physically represented in both approaches. Both methods showed similar qualitative behavior, such as defects originating along the impactor edges, a dominance of Shockley partial dislocations, and non-continuous dislocation distributions across the buried interface. The favorable comparison between methods justifies assumptions used in both, to model phenomena, such as the nucleation and interactions of single defects and partials with reflected tensile waves, based on MD predictions, which are consistent with the evolution of perfect and partial dislocation densities as predicted by DCP. This substantiates how the nanoscale as modeled by MD is representative of microstructural behavior as modeled by DCP.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sim-Situ: A Framework for the Faithful Simulation of in situ Processing

The amount of data generated by numerical simulations in various scientific domains led to a fundamental redesign of how the analysis and visualization of simulation outputs are performed. The throughput and capacity of storage subsystems have not evolved as fast as the computing power in extreme-scale supercomputers, making the classical post-hoc approach highly inefficient. In situ processing has then emerged as a solution in which simulation and data analysis/visualization are intertwined for better performance and greater interactivity.Determining the best allocation, i.e., how many resources to allocate to simulation and analysis respectively, mapping, i.e., where and at which frequency to run the analysis/visualization, and data transfer mode is a complex task whose performance assessment is crucial to the efficient execution of in situ processing. However, such a performance evaluation of different strategies usually relies either on directly running them on the targeted execution environments, which can rapidly become extremely time- and resource-consuming, or on resorting to simplified models of the components of an in situ application, which can lack of realism. In both cases, the validity of the performance evaluation is limited.In this paper, we present Sim-Situ, a simulation-based framework for the faithful performance evaluation of in situ processing strategies. We designed Sim-Situ to reflect the typical features of in situ processing systems. Thanks to its modular design, Sim-situ has the necessary flexibility to easily and faithfully evaluate the behavior and performance of various allocation, mapping, and data transfer strategies. We illustrate the simulation capabilities of Sim-Situ on a Molecular Dynamics use case. We study the impact of different strategies on performance and show how users can leverage Sim-Situ to determine interesting tradeoffs when adding analysis/visualization components to their application.

Honoré, Valentin↗
DOE OSTI2025

Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design

The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.

Mohan, Mood [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2022

LoTSS Jellyfish Galaxies. IV. Enhanced Star Formation on the Leading Half of Cluster Galaxies and Gas Compression in IC3949

With Mapping Nearby Galaxies at APO integral field spectroscopy, we present a resolved analysis of star formation for 29 jellyfish galaxies in nearby clusters, identified from radio continuum imaging taken by the Low Frequency Array. Simulations predict enhanced star formation on the "leading half" (LH) of galaxies undergoing ram pressure stripping, and in this work we report observational evidence for this elevated star formation. The dividing line (through the galaxy center) that maximizes this star formation enhancement is systematically tied to the observed direction of the ram-pressure-stripped tail, suggesting a physical connection between ram pressure and this star formation enhancement. We also present a case study on the distribution of molecular gas in one jellyfish galaxy from our sample, IC3949, using Atacama Large Millimeter/submillimeter Array CO J = 1 – 0, HCN J = 1 – 0, and HCO + J = 1 – 0 observations from the ALMA MaNGA Quenching and Star Formation Survey. The H2 depletion time (as traced by CO) in IC3949 ranges from ~1 Gyr in the outskirts of the molecular gas disk to ~11 Gyr near the galaxy center. IC3949 shows a clear region of enhanced star formation on the LH of the galaxy where the average depletion time is ~2.7 Gyr, in line with the median value for the galaxy on the whole. Dense gas tracers, HCN and HCO + , are only detected at the galaxy center and on the LH of IC3949. Our results favor a scenario in which ram pressure compresses the interstellar medium, promoting the formation of molecular gas that in turn fuels a localized increase of star formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Machine learning molecular dynamics simulations toward exploration of high-temperature properties of nuclear fuel materials: case study of thorium dioxide

Predicting materials properties of nuclear fuel compounds is a challenging task in materials science. Their thermodynamical behaviors around and above the operational temperature are essential for the design of nuclear reactors. However, they are not easy to measure, because the target temperature range is too high to perform various standard experiments safely and accurately. Moreover, theoretical methods such as first-principles calculations also suffer from the computational limitations in calculating thermodynamical properties due to their high calculation-costs and complicated electronic structures stemming from f-orbital occupations of valence electrons in actinide elements. Here, we demonstrate, for the first time, machine-learning molecular-dynamics to theoretically explore high-temperature thermodynamical properties of a nuclear fuel material, thorium dioxide. The target compound satisfies first-principles calculation accuracy because f-electron occupation coincidentally diminishes and the scheme meets sampling sufficiency because it works at the computational cost of classical molecular-dynamics levels. We prepare a set of training data using first-principles molecular dynamics with small number of atoms, which cannot directly evaluate thermodynamical properties but captures essential atomistic dynamics at the high temperature range. Then, we construct a machine-learning molecular-dynamics potential and carry out large-scale molecular-dynamics calculations. Consequently, we successfully access two kinds of thermodynamic phase transitions, namely the melting and the anomalous λ transition induced by large diffusions of oxygen atoms. Furthermore, we quantitatively reproduce various experimental data in the best agreement manner by selecting a density functional scheme known as SCAN. Our results suggest that the present scale-up simulation-scheme using machine-learning techniques opens up a new pathway on theoretical studies of not only nuclear fuel compounds, but also a variety of similar materials that contain both heavy and light elements, like thorium dioxide.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Deciphering the Cofilin Oligomers via Intermolecular Disulfide Bond Formation: A Coarse-Grained Molecular Dynamics Approach to Understanding Cofilin’s Regulation on Actin Filaments

Cofilin, a key actin-binding protein, orchestrates the dynamics of the actomyosin network through its actin-severing activity and by promoting the recycling of actin monomers. Recent experimental work suggests that cofilin also forms functionally distinct oligomers through thiol post-translational modification (PTM) that encourages actin nucleation and assembly. Despite these advances, the structural conformations of cofilin oligomers that modulate actin activity remain elusive because there are combinatorial ways to oxidize thiols in cysteines to form disulfide bonds rapidly. This study employs molecular dynamics simulations to investigate human cofilin 1 as a case study for exploring cofilin dimers via disulfide bond formation. Using the free energy profiling, our simulations unveil a range of probable cofilin dimer structures not represented in current Protein Data Bank entries. These candidate dimers are characterized by their distinct population distributions and relative free energies. Of particular note is a dimer featuring an interface between cysteines 139 and 147 residues, which demonstrates stable free energy characteristics and intriguingly symmetrical geometry. In contrast, the experimentally proposed dimer structure exhibits a less stable free energy profile. Here, we also evaluate frustration quantification based on the energy landscape theory in the protein-protein interactions at the dimer interfaces. Notably, the 39-39 dimer configuration emerges as a promising candidate for forming cofilin tetramers, as substantiated by frustration analysis. Additionally, docking simulations with actin filaments further evaluate the stability of these cofilin dimer-actin complexes. Our findings thus offer a computational framework for understanding the role of thiol post-translational modification cofilin proteins in regulating oligomerization, and the subsequent cofilin-mediated actin dynamics in the actomyosin network.

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

Strategies for community-sourced biocuration in bioinformatics: a case study on MIBiG 4.0

Biocuration is essential to transform molecular sequence data into standardized, machine-readable resources. Such curated datasets enable comparative analysis, predictive modeling, and data integration across bioinformatics platforms. While professional biocuration is resource-intensive and usually limited to institutional settings, community-driven approaches can mobilize large-scale annotation of specialized datasets and are more resilient to disruptions in scientific funding. Here, we present a model for community-powered curation applied to the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) repository. Through a framework of workflows for metadata capture, annotation validation, and contributor coordination, the MIBiG 4.0 initiative recruited 267 scientists across 178 institutions from 33 countries, volunteering an estimated 4000 h of work. These efforts expanded the MIBiG repository by 22% and enhanced its usability in downstream molecular data analyses in comparative genomic analyses, natural product discovery, and machine learning applications. We provide strategies and actionable lessons for adopting this model, supporting the sustainability of curated bioinformatics resources central to nucleic acid research and related fields.

biocuration↗
DOE OSTI2024

Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described.

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

Opportunities for fundamental physics research with radioactive molecules

Molecules containing short-lived, radioactive nuclei are uniquely positioned to enable a wide range of scientific discoveries in the areas of fundamental symmetries, astrophysics, nuclear structure, and chemistry. Recent advances in the ability to create, cool, and control complex molecules down to the quantum level, along with recent and upcoming advances in radioactive species production at several facilities around the world, create a compelling opportunity to coordinate and combine these efforts to bring precision measurement and control to molecules containing extreme nuclei. In this paper, we review the scientific case for studying radioactive molecules, discuss recent atomic, molecular, nuclear, astrophysical, and chemical advances which provide the foundation for their study, describe the facilities where these species are and will be produced, and provide an outlook for the future of this nascent field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

From Qualitative to Quantitative AOP: A Case Study of Neurodegeneration

Adverse outcome pathways (AOPs) include a sequence of events that connect a molecular-level initiating event with an adverse outcome at the cellular level for human health endpoints, or at the population level for ecological endpoints. When there is enough quantitative understanding of the relationships between key events in an AOP, a mathematical model may be developed to connect key events in a quantitative AOP (qAOP). Ideally, a qAOP will reduce the time and resources spent for chemical toxicity testing and risk assessment and enable the extrapolation of data collected at the molecular-level by in vitro assays, for example, to predict whether an adverse outcome may occur. Here, we review AOPs in the AOPWiki, an AOP repository, to determine best practices that would facilitate conversion from AOP to qAOP. Then, focusing on a particular case study, acetylcholinesterase inhibition leading to neurodegeneration, we describe specific methods and challenges. Examples of challenges include the availability and collection of quantitative data amenable to model development, the lack of studies that measure multiple key events, and model accessibility or transferability across platforms. We conclude with recommendations for improving key event and key event relationship descriptions in the AOPWiki that facilitate the transition of qualitative AOPs to qAOPs.

Sinitsyn, Dennis↗
DOE OSTI2021

Construction of Lattice Strain in Bimetallic Nanostructures and Its Effectiveness in Electrochemical Applications

Bimetallic nanocrystals (NCs), associated with various surface functions such as ligand effect, ensemble effect, and strain effect, exhibit superior electrocatalytic properties. The stress-induced surface strain effect can alter binding strength between the surface active sites and reactants as well as their intermediates, and the electrochemical performance of bimetallic NCs can be significantly facilitated by the lattice-strain modification via their morphologies, sizes, shell-thickness, surface defectiveness as well as compositions. Here in this review, an overview of fundamental principles, characterization techniques, and quantitative determination of the surface lattice strain is provided. Various strategies and synthesis efforts on creating lattice-strain-engineered bimetallic NCs, including the de-alloying process, atomic layer-by-layer deposition, thermal treatment evolution, one-pot synthesis, and other efforts are also discussed. It is further outlined how the lattice strain effect promotes electrochemical catalysis through the selected case studies. The reactions on oxygen reduction reaction, small molecular oxidation, water splitting reaction, and electrochemical carbon dioxide reduction reactions are focused. In particular, studies of lattice strain arisen from core–shell nanostructure and defectiveness are highlighted. Lastly, the potential challenges are summarized and the prospects of lattice-strain-based engineering on bimetallic nanocatalysts with suggestion and guidance of the future electrocatalyst design are envisioned.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Chemical Interpretation of Charged Point Defects in Semiconductors: A Case Study of Mg 2 Si

The electronic structures of charged point defects influence electrical and optical properties of semiconductors. Understanding the orbital interactions responsible for the electronic structures of defects therefore promotes a chemical intuition for defect-driven mechanisms in semiconductors. In this tutorial, we discuss a molecular orbital theory-based framework for understanding defect-induced electronic states based on local chemical interactions between the defect and the atoms surrounding the defect site. By using Mg 2 Si as a case study, we show how both the chemical interactions and molecular orbitals (i. e., wave functions) responsible for the charge state(s) of a defect can be understood from the bonding symmetry of the defect site. We anticipate that a chemistry-based perspective of charged defects will enrich defect engineering efforts for electronic and optical materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An ab initio molecular dynamics exploration of associates in Ba-Bi liquid with strong ordering trends

Fictive associates are widely used to describe and model liquid phases with strong ordering trends. However, little evidence is known about the assumed associates in most cases. In the present work, an ab initio molecular dynamics (AIMD) study is employed to investigate the characters of the Ba-Bi liquid, in which associates have been assumed in existing thermodynamic modeling. It is found that in the Ba rich melt, the Bi atoms are almost completely surrounded by Ba atoms. The Bi-centered coordination polyhedrons are strongly associated to crystalline structures of Ba 5 Bi 3 and Ba 4 Bi 3 with a longer lifetime than other polyhedrons during the AIMD simulations. In addition, these Bi-centered polyhedrons in Ba rich melt connect with each other through vertex, edge, face, and/or bipyramid sharing to form medium range orders (MRO). In the Bi rich melt, the Ba-centered polyhedrons also form MROs, but they are both structurally and compositionally diverse with a shorter lifetime. These findings from AIMD study provide evidences that there exist a strongly ordering Ba 4 Bi 3 associate and a weakly ordering BaBi 3 associate in the Ba-Bi liquid. Here, the predicted enthalpy of mixing in the liquid agrees well with the results by the CALPHAD modeling in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Perimatrix of middle ear cholesteatoma: A granulation tissue with a specific transcriptomic signature

Objectives/Hypothesis: To establish comprehensive transcriptomic profiles of cholesteatoma perimatrix tissue and granulation tissue from chronic otitis media (COM) that did not develop cholesteatoma, which can indicate molecular pathways involved in the cholesteatoma perimatrix pathology and invasiveness. Study Design: Retrospective Case Series. Methods: Transcriptome data were obtained from cholesteatoma perimatrix tissue and COM granulation tissue by an Illumina iScan microarray. Differentially expressed genes (DEGs) were subsequently analyzed using both bioinformatical functional annotation and network analysis. Expression of candidate genes (MMP9 and LCN2) was validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) on a larger group of samples. Results: Analysis of the transcriptome led to the identification of 169 differentially expressed genes between investigated tissues. Bioinformatic analysis suggested that most significant biological processes involving DEGs were previously described in cholesteatoma pathology. Network analysis identified ERBB2, TFAP2A, and TP63 as major hubs of the DEGs molecular network. Furthermore, it was observed that the cellular component most significantly enriched in DEGs was extracellular space containing 47 DEGs. Using qRT-PCR, it was confirmed that mRNA levels of the major extracellular hub (MMP9) are increased, whereas its interacting molecule (LCN2) mRNA levels were decreased in cholesteatoma perimatrix tissue compared to COM granulation tissue. Conclusions: The current study approach offers an overall look at molecular mechanisms that describe the cholesteatoma entity by focusing exclusively on the perimatrix processes in comparison to COM granulation tissue. The observed differences in gene expression between cholesteatoma perimatrix and COM granulation tissue could suggest novel markers potentially influenced by the perimatrix–matrix molecular interplay, which is not present in COM without cholesteatoma. Level of Evidence: NA. Laryngoscope, 130:E220–E227, 2020. © 2019 The American Laryngological, Rhinological and Otological Society, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Holo-omics for deciphering plant-microbiome interactions

Host-microbiome interactions are recognized for their importance to host health. An improved understanding of the molecular underpinnings of host-microbiome relationships will advance our capacity to accurately predict host fitness and manipulate interaction outcomes. Within the plant microbiome research field, unlocking the functional relationships between plants and their microbial partners is the next step to effectively using the microbiome to improve plant fitness. We propose that strategies that pair host and microbial datasets—referred to here as holo-omics—provide a powerful approach for hypothesis development and advancement in this area. We discuss several experimental design considerations and present a case study to highlight the potential for holo-omics to generate a more holistic perspective of molecular networks within the plant microbiome system. In addition, we discuss the biggest challenges for conducting holo-omics studies; specifically, the lack of vetted analytical frameworks, publicly available tools, and required technical expertise to process and integrate heterogeneous data. Finally, we conclude with a perspective on appropriate use-cases for holo-omics studies, the need for downstream validation, and new experimental techniques that hold promise for the plant microbiome research field. We argue that utilizing a holo-omics approach to characterize host-microbiome interactions can provide important opportunities for broadening system-level understandings and significantly inform microbial approaches to improving host health and fitness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Numerical Simulation of Vibrational Sum Frequency Generation Intensity for Non-Centrosymmetric Domains Interspersed in an Amorphous Matrix: A Case Study for Cellulose in Plant Cell Wall

Vibrational sum frequency generation (SFG) spectroscopy can specifically probe molecular species non-centrosymmetrically arranged in a centrosymmetric or isotropic medium. This capability has been extensively utilized to detect and study molecular species present at the two-dimensional (2D) interface at which the centrosymmetry or isotropy of bulk phases is naturally broken. The same principle has been demonstrated to be very effective for the selective detection of non-centrosymmetric crystalline nanodomains interspersed in three-dimensional (3D) amorphous phases. However, the full spectral interpretation of SFG features has been difficult due to the complexity associated with the theoretical calculation of SFG responses of such 3D systems. This paper describes a numerical method to predict the relative SFG intensities of non-centrosymmetric nanodomains in 3D systems as functions of their size and concentration as well as their assembly patterns, i.e., the distributions of tilt, azimuth, and rotation angles with respect to the lab coordinate. We applied the developed method to predict changes in the CH and OH stretch modes characteristic to crystalline cellulose microfibrils distributed with various orders, which are relevant to plant cell wall structures. As a result, the same algorithm can also be applied to any SFG-active nanodomains interspersed in 3D amorphous matrices.

36 MATERIALS SCIENCE↗