Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “molecular data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Insights into the Thermal Expansion of Amorphous Polymers

Here, we investigated the thermal expansion of amorphous polystyrene (PS) and poly(methyl methacrylate) (PMMA) homopolymers using the temperature dependence of spatial electron-density correlations. The strong and broad X-ray interference maxima arising from inter- and intramolecular correlation distances were distinct, maintaining their shape during the heating of the samples to 250 °C. Three maxima characteristic of electron density correlations between the backbones, substituents along the chain, and repeat-units were observed. A remarkable temperature dependence was identified in the largest correlation distances arising from the intermolecular interactions. Based on the temperature dependence of the correlation distances, the coefficients of molecular expansion were very close to the coefficients of thermal expansion. This study provides a simple yet accurate way to correlate macroscopic volume expansions with the molecular expansion obtained from wide-angle X-ray scattering (WAXS) data.

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

Connecting suborganismal data to bioenergetic processes: killifish embryos exposed to a dioxin-like compound

A core challenge for ecological risk assessment is to integrate molecular responses into a chain of causality to organismal or population level outcomes. Bioenergetic theory may be a useful approach for integrating suborganismal responses to predict organismal level responses that influence population dynamics. In this work, we describe a novel application of Dynamic Energy Budget (DEB), theory in the context of a toxicity framework (Adverse Outcome Pathways, AOP) to make quantitative predictions of chemical exposures to individuals, starting from suborganismal data. We use early life stage exposure of Fundulus heteroclitus to dioxin-like chemicals (DLCs) and connect AOP Key Events (KEs) to DEB processes through “damage” that is produced at a rate proportional to the internal toxicant concentration. We use transcriptomic data of fish embryos exposed to DLCs to translate molecular indicators of damage into changes in DEB parameters (damage increases somatic maintenance costs) and use DEB models to predict sublethal and lethal effects of young fish. By changing a small subset of model parameters, we predict the evolved tolerance to DLCs in some wild F. heteroclitus populations, a data set not used in model parameterization. The differences in model parameters points to reduced sensitivity and altered damage repair dynamics as contributing to this evolved resistance. Our methodology has potential extrapolation to untested chemicals of ecological concern.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

The Structural Characterisation and DFT-Aided Interpretation of Vibrational Spectra for Cyclo(l-Cys-d-Cys) Cyclic Dipeptide in a Solid State

Cyclic dipeptides with two intramolecular peptide bonds forming a six-membered 2,5-diketopiperazine ring are gaining significant attention due to their biological and chemical properties. Small changes in the local geometry of such molecules (from cis to trans) can lead to significant structural differences. This work presents the results of a study of cyclo(l-Cys-d-Cys), a dipeptide comprising two cysteine molecules in opposite chiral configurations, with the functional groups situated at both sides of the diketopiperazine ring. X-ray diffraction (XRD) experiment revealed that the molecule crystallises in the $P$-1 space group, which includes the centre of inversion. The IR and Raman vibrational spectra of the molecule were acquired and interpreted in terms of the potential energy distribution (PED) according to the results of density functional theory (DFT) calculations. The DFT-assisted analysis of energy frameworks for the hydrogen bond network within molecular crystals was performed to support the interpretation of X-ray structural data. The optimisation of the computational model based on three-molecule geometry sections from the crystallographic structure, selected to appropriately reflect the intermolecular interactions responsible for the formation of 1D molecular tapes in cyclo(l-Cys-d-Cys) crystal, allowed for better correspondence between theoretical and experimental vibrational spectra. This work can be considered the first complete structural characterisation of cyclo(l-Cys-d-Cys), complemented via vibrational spectroscopy results with full band assignment aided with the use of the DFT method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Determining best practices for using genetic algorithms in molecular discovery

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this tutorial, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. Here, we show that using a self-termination method with a minimum Spearman’s rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, a 50% elitism rate, three-way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

One thousand soils for molecular understanding of belowground carbon cycling

While significant progress has been made in understanding global carbon (C) cycling, the mechanisms regulating belowground C fluxes and storage are still uncertain. New molecular technologies have the power to elucidate these processes, yet we have no widespread standardized implementation of molecular techniques. To address this gap, we introduce the Molecular Observation Network (MONet), a decadal vision from the Environmental Molecular Sciences Laboratory (EMSL), to develop a national network for understanding the molecular composition, physical structure, and hydraulic and biological properties of soil and water. These data are essential for advancing the next generation of multiscale Earth systems models. In this paper, we discuss the 1000 Soils Pilot for MONet, including a description of standardized sampling materials and protocols and a use case to highlight the utility of molecular-level and microstructural measurements for assessing the impacts of wildfire on soil. While the 1000 Soils Pilot generated a plethora of data, we focus on assessments of soil organic matter (SOM) chemistry via Fourier-transform ion cyclotron resonance-mass spectrometry and microstructural properties via X-ray computed tomography to highlight the effects of recent fire history in forested ecosystems on belowground C cycling. We observed decreases in soil respiration, microbial biomass, and potential enzyme activity in soils with high frequency burns. Additionally, the nominal oxidation state of carbon in SOM increased with burn frequency in surface soils. This results in a quantifiable shift in the molecular signature of SOM and shows that wildfire may result in oxidation of SOM and structural changes to soil pore networks that persist into deeper soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Thermal exchange-correlation functionals: Capturing quantum electron behavior in warm, dense plasmas

We summarize and give perspective upon recent progress in developing non-empirical constraint-based thermal (i.e., free energy) exchange-correlation (XC) density functionals essential for accurate description of the quantum behavior of electrons in warm, dense plasmas. After delineating the critical role of ground-state functionals for zero-temperature, time-dependent DFT, we outline the underpinnings of local density approximation, generalized gradient approximation (GGA), and meta-GGA XC free-energy functionals. Two basic thermalization principles for upgrading ground-state XC functionals to successful thermal ones are emphasized. Then, we turn to a long-standing challenge, assessment of the accuracy of well-founded functionals. Unlike the ground state, there are a few exact results for large T and P. An exception is path integral Monte Carlo (PIMC) data for dense H/D and He plasmas. For those, we did ab initio molecular dynamics simulations under selected thermodynamic conditions employing five thermal XC functionals: two approximate thermal GGAs, fully thermal GGA, an approximate meta-GGA, and fully thermal meta-GGA. Comparisons with the PIMC data show that functionals thermalized by augmenting a non-thermal functional with a lower-level thermal contribution are inferior to functionals with thermal XC and spatial inhomogeneity effects taken into account at the same level of refinement. We believe this and similar evidence should be convincing to the high-energy density physics community of the necessity of use of proper thermal XC functionals in simulation studies of finite-temperature quantum effects in warm, dense plasmas.

Ab-initio molecular dynamics↗
DOE OSTI2023

Modeling the aqueous interface of amorphous TiO 2 using deep potential molecular dynamics

Amorphous titanium dioxide (a-TiO 2 ) is widely used as a coating material in applications such as electrochemistry and self-cleaning surfaces where its interface with water has a central role. However, little is known about the structures of the a-TiO 2 surface and aqueous interface, particularly at the microscopic level. In this work, we construct a model of the a-TiO 2 surface via a cut-melt-and-quench procedure based on molecular dynamics simulations with deep neural network potentials (DPs) trained on density functional theory data. After interfacing the a-TiO 2 surface with water, we investigate the structure and dynamics of the resulting system using a combination of DP-based molecular dynamics (DPMD) and ab initio molecular dynamics (AIMD) simulations. Both AIMD and DPMD simulations reveal that the distribution of water on the a-TiO 2 surface lacks distinct layers normally found at the aqueous interface of crystalline TiO 2 , leading to an ~10 times faster diffusion of water at the interface. Bridging hydroxyls (Ti 2 –O b H) resulting from water dissociation decay several times more slowly than terminal hydroxyls (Ti–O w H) due to fast Ti–O w H 2 → Ti–O w H proton exchange events. These results provide a basis for a detailed understanding of the properties of a-TiO 2 in electrochemical environments. Moreover, the procedure of generating the a-TiO 2 -interface employed here is generally applicable to studying the aqueous interfaces of amorphous metal oxides.

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

Structural Implications of Interfacial Hydrogen Bonding in Hydrated Wyoming-Montmorillonite Clay

Montmorillonite (MMT) clay - a layered porous nanomaterial used as seals in engineered waste containment barriers for spent nuclear fuel - adopts discrete hydration/swelling states depending upon surrounding water and ion activities and confining pressure. The structure of nanoconfined water and charge-balancing counterions in the clay mineral interlayers dictate the swelling and mechanical behavior of MMT, so a molecular model for this clay with high structural fidelity is required to accurately predict the reliability of long-term nuclear waste storage. Here, we present a molecular model for MMT that is based on high resolution transmission electron microscopy of Wyoming-MMT single crystals. Imaging data unambiguously show a cis-vacant arrangement of structural hydroxyl groups in the octahedral sheet, whereas existing molecular models assume a centrosymmetric trans-vacant configuration for MMT. Furthermore, using atomistic simulations, we find that the cis-vacant arrangement of structural hydroxyl groups significantly affects the structure of adsorbed water yielding a larger population of hydrogen bonds with bridging oxygens on the tetrahedral sheet and weak hydrogen bonding between the hydroxyl groups in the octahedral sheet and water in the clay mineral interlayers. As a result, water adsorbed in the interlayer is more "ice-like", with stronger ordering and lower density, although the diffusivity of the interlayer species is not significantly diminished. Our improved structural model for MMT provides insight into the energetics of water adsorption, which ultimately dictates its pore- to macro-scale swelling, transport, and fracture properties.

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

Evidence for Dissociation and Ionization in Shock Compressed Nitrogen to 800 GPa

Triple bonding in the nitrogen molecule (N 2 ) is among the strongest chemical bonds with a dissociation enthalpy of 9.8 eV/molecule. Nitrogen is therefore an excellent test bed for theoretical and numerical methods aimed at understanding how bonding evolves under the influence of the extreme pressures and temperatures of the warm dense matter regime. Here, we report laser-driven shock experiments on fluid molecular nitrogen up to 800 GPa and 4.0 g/cm 3 . Line-imaging velocimetry measurements and impedance matching method with a quartz reference yield shock equation of state data of initially precompressed nitrogen. Comparison with numerical simulations using path integral Monte Carlo and density functional theory molecular dynamics reveals clear signatures of chemical dissociation and the onset of L-shell ionization. Furthermore, combining data along multiple shock Hugoniot curves starting from densities between 0.76 and 1.29 g/cm 3 , our study documents how pressure and density affect these changes in chemical bonding and provides benchmarks for future theoretical developments in this regime, with applications for planetary interior modeling, high energy density science, and inertial confinement fusion research.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D

Abstract Now in its 52nd year of continuous operations, the Protein Data Bank (PDB) is the premiere open‐access global archive housing three‐dimensional (3D) biomolecular structure data. It is jointly managed by the Worldwide Protein Data Bank (wwPDB) partnership. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is funded by the National Science Foundation, National Institutes of Health, and US Department of Energy and serves as the US data center for the wwPDB. RCSB PDB is also responsible for the security of PDB data in its role as wwPDB‐designated Archive Keeper. Every year, RCSB PDB serves tens of thousands of depositors of 3D macromolecular structure data (coming from macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro‐electron diffraction). The RCSB PDB research‐focused web portal ( RCSB.org ) makes PDB data available at no charge and without usage restrictions to many millions of PDB data consumers around the world. The RCSB PDB training, outreach, and education web portal ( PDB101.RCSB.org ) serves nearly 700 K educators, students, and members of the public worldwide. This invited Tools Issue contribution describes how RCSB PDB (i) is organized; (ii) works with wwPDB partners to process new depositions; (iii) serves as the wwPDB‐designated Archive Keeper; (iv) enables exploration and 3D visualization of PDB data via RCSB.org ; and (v) supports training, outreach, and education via PDB101.RCSB.org . New tools and features at RCSB.org are presented using examples drawn from high‐resolution structural studies of proteins relevant to treatment of human cancers by targeting immune checkpoints.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Approach to using 3D laser-induced breakdown spectroscopy (LIBS) data to explore the interaction of FLiNaK and FLiBe molten salts with nuclear-grade graphite

Nuclear graphite has historically been a key component of many nuclear reactor designs and has emerged as key to numerous advanced nuclear reactor design concepts. Molten salt reactors (MSRs) are one broad group of advanced reactor designs currently being pursued by industry for commercialization. Several MSR designs under consideration use graphitic materials that directly interface with a molten salt, whether it is a fuel salt, coolant salt, or both. Therefore, the interaction of graphite materials with molten salts must be understood. To gain this required understanding, a range of data is needed including porosity, strength, and composition as a function of different salt exposure parameters. In this study, a laser-induced breakdown spectroscopy (LIBS) measurement and data analysis methodology was developed to obtain spatially resolved elemental composition information for graphite samples exposed to a molten fluoride salt. Traditional univariate emission line analysis of atomic, ionic, and molecular optical emission signals was coupled via correlation analysis with spectral decomposition of the data using principal component analysis. Elemental depth profiling and elemental mapping were also performed to visualize salt–graphite interactions. LIBS was demonstrated to be useful for measuring key analytes such as fluorine and hydrogen, which are troublesome for other analysis techniques. Evidence for complex behavior was found, thereby demonstrating the usefulness of the developed approach for future systematic studies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Organic matter concentration and composition of experimentally burned open air and muffle furnace vegetation chars across differing burn severity and feedstock types from Pacific Northwest, USA (v4).

This dataset represents results from an experimental study designed to compare how the chemical composition of organic matter changes across different burn conditions and feedstock materials. The dataset provides both solid and dissolved phase bulk concentration and organic matter characterization data from experimentally generated chars. Chars were created in a closed muffle furnace or on an open burn table from four different feedstock species representing vegetation commonly impacted by fire regimes across the Pacific Northwest, USA. This data can be used to compare how different burn conditions may influence resultant organic matter chemistry and help further our understanding of potential biogeochemical impacts on river corridors post-fire. This dataset is comprised of one data package readme, one data dictionary (dd), one file level metadata (flmd), fourteen burn table videos, burn table video metadata and three folders containing (A) data; (B) metadata and protocols; and (C) photos. The folder names and the file name of the data package readme include a version number which will be updated with future iterations of this data package. The data folder includes (1) solid carbon and solid nitrogen; (2) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) and total dissolved nitrogen (TN); (3) pH; (4) thermocouple time series temperature; (5) methods codes; (6) installation methods; (7) excitation emissions matrix (EEM) methods information; (8) a folder of excitation emissions matrix (EEM) fluorescence and absorbance spectra in dissolved organic matter and EEMs processing instructions; (9) solid state carbon-13 and solution state phosphorus nuclear magnetic resonance (13-C NMR and 31-P NMR) data and methods; (10) benzene polycarboxylic acid (BPCA) concentration and stable isotope data; (11) FTICR-MS methods; (12) Inductively coupled plasma (ICP) data for total calcium, magnesium, iron, aluminum, potassium, phosphorus, sodium, and sulfur along with sodium hydroxide-ethylenediaminetetraacetic acid (sodium hydroxide-EDTA) extractable calcium, magnesium, iron, aluminum, potassium, phosphorus, and sulfur; (13) a folder of phosphorus, carbon, and nitrogen X-ray absorption near edge structure (P-XANES, N-XANES, C-XANES) data for samples and standards; (14) P-XANES, N-XANES, C-XANES methods; (15) molybdate reactive phosphorus; and (16) folder of high resolution characterization of organic matter via 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). The FTICR folder contains .txt data files and a subfolder containing instruments for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. The metadata and protocols folder includes (1) international geo-sample number (IGSN) mapping file (2) burn and laboratory metadata; (3) burn protocol; (4) laboratory protocol; (5) vegetation collection metadata; and (6) vegetation collection protocol. The folder contains photos of the solid chars. All files are .csv, .txt, .pdf, .jpg, .jpeg, .R, .ref, or .mp4. The data package was originally published October 2022 (v1). It was updated April 2023 (v2; new data files), September 2023 (v3; new and corrected data files), and September 2024 (v4; new and added/updated files). Metadata files were also updated to reflect these changes. See the change history section in the readme for more details.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Design and Operation of a Multi-Bed Catalytic Micro-Reactor for the Study of Co-Processing of Bio-Oils with VGO

An industry wide shift from fossil-based fuel to renewable fuel sources including biomass, municipal waste, and plastics will require new process monitoring methods to minimize transitional risks including off specification product formation and catalyst deactivation. This project aims to provide a machine learning based process monitoring tool composed of online, slipstream mass spectra for use in biomass refineries and co-processing in existing refineries allowing operators to monitor product qualities and adjust process conditions accordingly. In order to maximize the robustness of the tool, large volumes of data must be collected to fine tune model parameters which consists of both micro and pilot scale mass spectral data. Micro-scale data is collected with a multi-tube micro-reactor housing up to six catalysts in horizontal beds, coupled with a molecular beam mass spectrometer. A pyrolizer equipped with an auto-sampler streamlines the micro-scale data collection process. This type of pyrolizer/micro-reactor configuration does not exist on the market, and therefore had to be created for the purposes of this project. The design and commissioning of this reactor will be presented in detail. This reactor set-up is highly flexible and increases throughput of analysis. For catalyst testing, each bed can be individually selected simply by turning valves. For catalyst reduction and regeneration, simultaneous flow through all six beds is used. The reproducibility of the system was first assessed with whole biomass pyrolysis along with pyrolysis of calibration standards. Initial work on this system evaluated two FCC catalysts, equilibrium catalyst (E-cat), and a proprietary catalyst from Johnson Matthey specifically design for co-processing of bio-oil with vacuum gas oil (VGO). This work used model compounds and VGO which illuminated differences in products produced by the catalysts.

biomass↗
DOE OSTI2023

Cation–polymer interactions and local heterogeneity determine the relative order of alkali cation diffusion coefficients in PEGDA hydrogels

Current research efforts are focused on endowing polymer membranes with ion–ion selectivity by incorporating ion–polymer interactions into materials to bias the selective partitioning and or diffusivity of one species over another. However, little is known about the impact of such interactions on the mechanisms of ion transport. In this study, we probe the influence of cation–polymer interactions on cation, anion, and salt diffusivity in a model membrane material, poly(ethylene glycol) diacrylate (PEGDA) by modeling concentrated polyethylene oxide solutions via molecular dynamics simulations. These results are compared to published experimental data for LiCl, NaCl, and KCl diffusion in PEGDA. Experimentally, the order of salt and cation diffusion coefficients for LiCl, NaCl, and KCl deviate from the order in aqueous solutions. Here, simulations identify these deviations to arise from cation–polymer coordination in the membrane. Both the fraction of bound cations and the average binding lifetime increases with decreasing cation hydration free energy (moving down the alkali series), leading to different diffusivity trends in the membrane compared to solution. However, to recover the experimentally observed order of diffusivities cations and salt in our simulations, we needed to incorporate membrane heterogeneity explicitly via a polymer charge scaling procedure. Together, our results indicate that cation–polymer interactions, as well as spatial heterogeneity within the membrane, play a critical role in dictating the observed order of alkali cation and salt diffusion coefficients in membranes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models

Abstract Background A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. Results Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp 614 Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro 323 Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. Conclusions These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Laser-Plasma Spectroscopy of Hydroxyl with Applications

This article discusses laser-induced laboratory-air plasma measurements and analysis of hydroxyl (OH) ultraviolet spectra. The computations of the OH spectra utilize line strength data that were developed previously and that are now communicated for the first time. The line strengths have been utilized extensively in interpretation of recorded molecular emission spectra and have been well-tested in laser-induced fluorescence applications for the purpose of temperature inferences from recorded data. Moreover, new experiments with Q-switched laser pulses illustrate occurrence of molecular recombination spectra for time delays of the order of several dozen of microseconds after plasma initiation. The OH signals occur due to the natural humidity in laboratory air. Centrifugal stretching of the Franck-Condon factors and r-centroids are included in the process of determining the line strengths that are communicated as a Supplementary File. Laser spectroscopy applications of detailed OH computations include laser-induced plasma and combustion analyses, to name but two applications. This work also includes literature references that address various diagnosis applications.

47 OTHER INSTRUMENTATION↗