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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations

Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biosynthesizable, biocompatible, and biodegradable polymers to replace petroleum-based plastics for addressing the global plastic pollution problem. Although PHAs offer a wide range of chemical diversity, the structure–property relationships in this class of polymers remain poorly established. In particular, the available experimental data on the mechanical properties is scarce. In this contribution, we have used molecular dynamics simulations employing a recently developed forcefield to predict chemical trends in mechanical properties of PHAs. Specifically, we make predictions for Young’s modulus, and yield stress for a wide range of PHAs that exhibit varying lengths of backbone and side chains as well as different side chain functional groups. Deformation simulations were performed at six different strain rates and six different temperatures to elucidate their influence on the mechanical properties. Our results indicate that Young’s modulus and yield stress decrease systematically with increase in the number of carbon atoms in the side chain as well as in the polymer backbone. In addition, we find that the mechanical properties were strongly correlated with the chemical nature of the functional group. The functional groups that enhance the interchain interactions lead to an enhancement in both the Young’s modulus and yield stress. Finally, we applied the developed methodology to study composition-dependence of the mechanical properties for a selected set of binary and ternary copolymers. Overall, our work not only provides insights into rational design rules for tailoring mechanical properties in PHAs, but also opens up avenues for future high throughput atomistic simulation studies geared towards identifying functional PHA polymer candidates for targeted applications.

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

Diffraction data from aerosolized Coliphage PR772 virus particles imaged with the Linac Coherent Light Source

Single Particle Imaging (SPI) with intense coherent X-ray pulses from X-ray free-electron lasers (XFELs) has the potential to produce molecular structures without the need for crystallization or freezing. Here we present a dataset of 285,944 diffraction patterns from aerosolized Coliphage PR772 virus particles injected into the femtosecond X-ray pulses of the Linac Coherent Light Source (LCLS). Additional exposures with background information are also deposited. The diffraction data were collected at the Atomic, Molecular and Optical Science Instrument (AMO) of the LCLS in 4 experimental beam times during a period of four years. The photon energy was either 1.2 or 1.7 keV and the pulse energy was between 2 and 4 mJ in a focal spot of about 1.3 μm x 1.7 μm full width at half maximum (FWHM). The X-ray laser pulses captured the particles in random orientations. The data offer insight into aerosolised virus particles in the gas phase, contain information relevant to improving experimental parameters, and provide a basis for developing algorithms for image analysis and reconstruction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

RhizoGrid Indexed Sorghum Rhizosphere Multi-Omics

PerCon SFA project data dentification of spatially resolved biomarkers of drought in Sorghum bicolor rhizosphere molecular-microbe interactions using a novel root cartography "RhizoGrid" system for sampling plants under drought and control conditions across 10 equally sized root zone environments (4 quadrants each). Each quadrant was sampled and processed for 16S amplicon, metabolomics, and X-ray computed tomography (XCT). Data download includes experimental metadata and results files for 16S rRNA sequence analysis of microbial community assembly (processed data files), liquid chromatography mass spectrometry (LC-MS) metabolomics analysis of microbial community root exudates (processed data files), X-ray computed tomography (XCT) spatial gradient analysis (raw and processed data files) of microbial community composition, and related computational modeling outputs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction

Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structure–property mappings, these models require large amounts of data which can be a challenge to collect given the time and resource-intensive nature of experimental material characterization efforts. Additionally, such models fail to generalize to new types of molecular structures that were not included in the model training data. The acceleration of material development through uncertainty-guided experimental design has the promise to significantly reduce the data requirements and enable faster generalization to new types of materials. To evaluate the potential of such approaches for electrolyte design applications, we perform comprehensive evaluation of existing uncertainty quantification methods on the prediction of two relevant molecular properties - aqueous solubility and redox potential. We develop novel evaluation methods to probe the utility of the uncertainty estimates for both in-domain and out-of-domain data sets. Finally, we leverage selected uncertainty estimation methods for active learning to evaluate their capacity to support experimental design.

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

The denatured state of HIV ‐1 protease under native conditions

Abstract The denatured state of several proteins has been shown to display transient structures that are relevant for folding, stability, and aggregation. To detect them by nuclear magnetic resonance (NMR) spectroscopy, the denatured state must be stabilized by chemical agents or changes in temperature. This makes the environment different from that experienced in biologically relevant processes. Using high‐resolution heteronuclear NMR spectroscopy, we have characterized several denatured states of a monomeric variant of HIV‐1 protease, which is natively structured in water, induced by different concentrations of urea, guanidinium chloride, and acetic acid. We have extrapolated the chemical shifts and the relaxation parameters to the denaturant‐free denatured state at native conditions, showing that they converge to the same values. Subsequently, we characterized the conformational properties of this biologically relevant denatured state under native conditions by advanced molecular dynamics simulations and validated the results by comparison to experimental data. We show that the denatured state of HIV‐1 protease under native conditions displays rich patterns of transient native and non‐native structures, which could be of relevance to its guidance through a complex folding process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Local-Field Effects in Linear Response Properties within a Polarizable Frozen Density Embedding Method

In this work, we present a polarizable frozen density embedding (FDE) method for calculating polarizabilities of coupled subsystems. The method (FDE-pol) combines a FDE method with an explicit polarization model such that the expensive freeze/thaw cycles can be bypassed, and approximate nonadditive kinetic potentials are avoided by enforcing external orthogonality between the subsystems. To describe the polarization of the frozen environment, we introduce a Hirshfeld partition-based density-dependent method for calculating the atomic polarizabilities of atoms in molecules, which alleviates the need to fit the atomic parameters to a specific system of interest or to a larger general set of molecules. Further, we show that the Hirshfeld partition-based method predicts molecular polarizabilities close to the basis set limit, and thus, a single basis set-dependent scaling parameter can be introduced to improve the agreement against the reference polarizability data. To test the model, we characterized the uncoupled and coupled response of small interacting molecular complexes. Here, the coupled response properties include the perturbation of the frozen system due to the external perturbation which is ignored in the uncoupled response. We show that FDE-pol can accurately reproduce both the exact uncoupled polarizability and the coupled polarizabilities of the supermolecular systems. Using damped response theory, we also demonstrate that the coupled frequency-dependent polarizability can be described by including local field effects. The results emphasize the necessity of including local-field effects for describing the response properties of coupled subsystems, as well as the importance of accurate atomic polarizability models.

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

Data-Driven Strategies for Accelerated Materials Design

The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Systematic modification of functionality in disordered elastic networks through free energy surface tailoring

A combined machine learning–physics–based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system’s free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity and points to its potential for design of complex molecular systems.

42 ENGINEERING↗
DOE OSTI2025

Experimental and Computational Insights into the Structural Dynamics of the Fc Fragment of IgG1 Subtype from Biosimilar VEGF‐Trap

The constant fragment (Fc) of the immunoglobulin G1 (IgG1) subtype is increasingly recognized as a crucial scaffold in the development of advanced therapeutics due to its enhanced specificity, efficacy, and extended half‐life. A prime example is VEGF‐Trap (Aflibercept), a recombinant fusion protein that merges the Fc region of the IgG1 subtype with the binding domains of vascular endothelial growth factor receptors (VEGFR)‐1 and VEGFR‐2. The Fc region's role in N‐glycosylation is particularly important, as it significantly influences protein stability. Herein, the first near‐physiological temperature structures of the N‐glycan‐bound Fc fragment of IgG1 subtype from a biosimilar VEGF‐Trap are presented, determined using the SPring‐8 Angstrom Compact free electron LAser (SACLA) and the Turkish Light Source (Turkish DeLight). Comparative analysis with cryogenic structures, including existing data, reveals alternate conformations within the glycan‐binding pocket. Furthermore, molecular dynamics simulations indicate the presence of a high degree of structural plasticity, explaining how the protein adapts its structure through conformational changes. The observed structural fluctuations/conformational changes demonstrate the effect of N‐glycans on protein stability. These findings offer new insights into the molecular basis of Fc‐mediated functions and provide valuable information for the design of next‐generation therapeutics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Regimes of evaporation and mixing behaviors of nanodroplets at transcritical conditions

The objective of this paper is to examine the fundamental mechanisms responsible for the transition between subcritical evaporation and supercritical dense-fluid-mixing in the absence of convection effects, specifically focusing on the liquid–vapor interfacial dynamics. To isolate the dynamics of this transition process, we characterize the different physical behaviors exhibited by an -dodecane nanoscale droplet placed in different nitrogen ambient conditions across the fuel’s critical point. We employ a continuum-based interface-resolving diffuse-interface method to explore the underlying phase-exchange mechanisms that bring about such distinct dynamics. Following the comparison against molecular dynamics simulations and experiments of evaporating droplets and experimental data for vapor–liquid equilibria, a parametric study at various ambient conditions and droplet sizing is performed to identify four regimes of evaporation/mixing behaviors: sub- and supercritical droplet evaporation, and sub- and supercritical dense-fluid-mixing. It is shown that the distinction in the phase-exchange mechanisms in these four regimes are brought about by the different thermodynamic phases the droplet center can exhibit during the evaporation/mixing process: subcritical liquid, supercritical liquid-like, subcritical gaseous, and supercritical gas-like, respectively. It is shown that the subcritical dense-fluid-mixing behavior is a direct result of nanoconfinement of the liquid–vapor interfacial structure and thus is not present for large droplet sizes. Finally, the present study also shows that the supercritical phase-exchange dynamics can follow two different pathways: supercritical droplet-like evaporation and supercritical dense-fluid-mixing. Furthermore, promoting the early transition to supercritical dense-fluid-mixing can significantly expedite the phase-exchange process through the disintegration of the liquid-like droplet core.

42 ENGINEERING↗
DOE OSTI2024

Structural basis for C-degron selectivity across KLHDCX family E3 ubiquitin ligases

Abstract Specificity of the ubiquitin-proteasome system depends on E3 ligase-substrate interactions. Many such pairings depend on E3 ligases binding to peptide-like sequences - termed N- or C-degrons - at the termini of substrates. However, our knowledge of structural features distinguishing closely related C-degron substrate-E3 pairings is limited. Here, by systematically comparing ubiquitylation activities towards a suite of common model substrates, and defining interactions by biochemistry, crystallography, and cryo-EM, we reveal principles of C-degron recognition across the KLHDCX family of Cullin-RING ligases (CRLs). First, a motif common across these E3 ligases anchors a substrate’s C-terminus. However, distinct locations of this C-terminus anchor motif in different blades of the KLHDC2, KLHDC3, and KLHDC10 β-propellers establishes distinct relative positioning and molecular environments for substrate C-termini. Second, our structural data show KLHDC3 has a pre-formed pocket establishing preference for an Arg or Gln preceding a C-terminal Gly, whereas conformational malleability contributes to KLHDC10’s recognition of varying features adjacent to substrate C-termini. Finally, additional non-consensus interactions, mediated by C-degron binding grooves and/or by distal propeller surfaces and substrate globular domains, can substantially impact substrate binding and ubiquitylatability. Overall, the data reveal combinatorial mechanisms determining specificity and plasticity of substrate recognition by KLDCX-family C-degron E3 ligases.

Science & Technology - Other Topics↗
DOE OSTI2020

Spatial separation of triplet excitons drives endothermic singlet fission

Molecules that undergo singlet fission, converting singlet excitons into pairs of triplet excitons, have potential as photovoltaic materials. The possible advantages of endothermic singlet fission (enhanced use of photon energy and larger triplet energies for coupling with common absorbers) motivated us to assess the role of exciton delocalization in the activation of this process. Here we report the synthesis of a series of linear perylene oligomers that undergo endothermic singlet fission and have endothermicities in the range 5-10 kBT at room temperature in solution. We study these compounds using transient spectroscopy and modelling to unravel the singlet and triplet dynamics. We show that the minimal number of coupled chromophores needed to undergo endothermic singlet fission is three, which provides sufficient statistical space for triplet excitons to separate and avoid annihilation - and a subsequent fast return to the singlet state. Our data additionally suggest that torsional motion of chromophores about the molecular axis following triplet-pair separation contributes to the increase in entropy, thus lengthening the triplet lifetime in longer oligomers.

14 SOLAR ENERGY↗
DOE OSTI2022

Elucidation of the physical factors that control activated transport of penetrants in chemically complex glass-forming liquids

Understanding the activated transport of penetrant or tracer atoms and molecules in condensed phases is a challenging problem in chemistry, materials science, physics, and biophysics. Many angstrom- and nanometer-scale features enter due to the highly variable shape, size, interaction, and conformational flexibility of the penetrant and matrix species, leading to a dramatic diversity of penetrant dynamics. Based on a minimalist model of a spherical penetrant in equilibrated dense matrices of hard spheres, a recent microscopic theory that relates hopping transport to local structure has predicted a novel correlation between penetrant diffusivity and the matrix thermodynamic dimensionless compressibility, S 0 ( T ) (which also quantifies the amplitude of long wavelength density fluctuations), as a consequence of a fundamental statistical mechanical relationship between structure and thermodynamics. Moreover, the penetrant activation barrier is predicted to have a factorized/multiplicative form, scaling as the product of an inverse power law of S 0 ( T ) and a linear/logarithmic function of the penetrant-to-matrix size ratio. This implies an enormous reduction in chemical complexity that is verified based solely on experimental data for diverse classes of chemically complex penetrants dissolved in molecular and polymeric liquids over a wide range of temperatures down to the kinetic glass transition. The predicted corollary that the penetrant diffusion constant decreases exponentially with inverse temperature raised to an exponent determined solely by how S 0 ( T ) decreases with cooling is also verified experimentally. Our findings are relevant to fundamental questions in glassy dynamics, self-averaging of angstrom-scale chemical features, and applications such as membrane separations, barrier coatings, drug delivery, and self-healing.

Science & Technology - Other Topics↗
DOE OSTI2024

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

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

XFEL SASE pulses can enhance time-dependent observables

Abstract X-ray free electron lasers (XFELs) have emerged as powerful sources of short and intense x-ray pulses. We propose a simple and robust procedure which takes advantage of the inherent stochasticity of self-amplified stimulated emission (SASE) pulses to enhance the time-resolution and signal strength of the recorded data. Notably, the proposed method is able to enhance the average signal without knowledge of the signal strength of individual shots. Simple metrics for the probe pulses are introduced, such as an effective pulse duration applicable to SASE pulses characterised in the time domain using e.g. an X-band transverse cavity. The approach is evaluated using simulated and real pulse data in the context of ultrafast electron dynamics in a molecule. Utilising H 2 as a model system, we demonstrate the efficacy of the method theoretically, successfully enhancing the predicted nonresonant ultrafast x-ray scattering signal associated with electron dynamics. The method presented is broadly applicable and offers a general strategy for enhancing time-dependent observables at XFELs.

data analysis↗
DOE OSTI2021

Electronic properties of epitaxial La 1- x Sr x RhO 3 thin films

Here, we report on the synthesis and electronic properties of epitaxial perovskite La 1- x Sr x RhO 3 thin films. Thin films with a Sr content ranging from $\textit{x}$ = 0 to 0.5 have been grown using molecular beam epitaxy. Transport and x-ray photoemission spectroscopy data reveal an insulator-metal-insulator transition, accompanied by a $\textit{p-}$ to $\textit{n}-$ type carrier change observed in Hall measurements. Combined with theoretical calculations, we find that the addition of Sr does not directly dope carriers into the conduction band, but rather induces localized Rh 4$\textit{d}$ states within the LaRhO 3 band gap. The bandwidth of the impurity band increases with Sr content, eventually causing the valence band (VB) and the localized Rh 4$\textit{d}$ band to overlap, which explains the first insulator-to-metal transition occurring at $\textit{x}$ = 0.35. For Sr content $\textit{x}$ > 0.4, possible cation ordering results in an increase of the gap between the VB and the Rh 4$\textit{d}$ band, leading to the second metal-to-insulator transition. We map out the electronic phase diagram of the Sr-doped LaRhO 3 system and suggest strategies to engineer the electronic states in rhodate systems via delocalizing the Rh 3+ states.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Comparative transcriptomics uncovers poplar and fungal genetic determinants of ectomycorrhizal compatibility

Ectomycorrhizal symbiosis supports tree growth and is crucial for nutrient cycling and temperate and boreal ecosystems functioning. The establishment of functional ectomycorrhiza (ECM) first requires the association of compatible partners. However, host and fungal genetic determinants governing mycorrhizal compatibility are unknown. To identify such factors in poplar and its fungal associates, we mined existing and de novo tree and fungal transcriptional datasets. We identified co-expressed genes enabling ECM symbiosis at early and mature stages of the interaction. These sets of genes can be divided into general fungal-sensing and ECM-specific components. We highlight the importance of fungal modulation of plant JA-related defenses and the regulation of secretory pathways for ECM compatibility, including upregulation of key fungal small secreted proteins, the downregulation of plant secreted peroxidases, and the downregulation of plant cell wall remodeling proteins concomitantly with the upregulation of fungal glycosyl hydrolases acting on pectin. Not only gene regulation, but also its temporal scale and dynamics seem to play a crucial role for mycorrhizal compatibility. The expression profile of the host Common Symbiosis Pathway and nutrient transporters was also studied, revealing constitutive levels of expression and moderate upregulation in compatible ECM interactions. Overall, these results underscore the importance of novel biological functions during the establishment of ECM symbiosis, help us gain insights into the molecular events determining mycorrhiza compatibility, and serve as a data-rich transcriptomic resource to open new research questions in the field.

Marqués‐Gálvez, José Eduardo↗