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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computational identification of HCV neutralizing antibodies with a common HCDR3 disulfide bond motif in the antibody repertoires of infected individuals

Despite recent success in hepatitis C virus (HCV) treatment using antivirals, an HCV vaccine is still needed to prevent reinfections in treated patients, to avert the emergence of drug-resistant strains, and to provide protection for people with no access to the antiviral therapeutics. The early production of broadly neutralizing antibodies (bNAbs) associates with HCV clearance. Several potent bNAbs bind a conserved HCV glycoprotein E2 epitope using an unusual heavy chain complementarity determining region 3 (HCDR3) containing an intra-loop disulfide bond. Isolation of additional structurally-homologous bNAbs would facilitate the recognition of key determinants of such bNAbs and guide rational vaccine design. Here we report the identification of new antibodies containing an HCDR3 disulfide bond motif using computational screening with the Rosetta software. Using the newly-discovered and already-known members of this antibody family, we review the required HCDR3 amino acid composition and propose determinants for the bent versus straight HCDR3 loop conformation observed in these antibodies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Investigation of Rare Earth Elements Mobility Using Molecular Dynamics

Rare Earth Elements (REE) are critical many applications such as electronics, permanent magnets, agricultural etc. Effective separation of REEs is a challenge due to their similarities in chemical and physical properties. Current separation processes are costly and environmentally detrimental. Therefore, research on REEs mobility in water-based solvents is necessary to design effective separation processes. In this research, the key kinetic and thermodynamic parameters, including diffusion coefficient and hydration shell are determined under the influence of external electric field. The diffusion coefficient of metal ions increases due to electric field in pure water.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization

Redox flow batteries (RFBs) are a promising technology for stationary energy storage applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried in flowable redoxactive materials (redoxmers) which are stored externally and pumped to the cell during operation. Further improvements in the energy density of RFBs necessitates redoxmer designs with wider redox potential windows and higher solubility. Additionally, designing redoxmers with a fluorescence-enabled self-reporting functionality allows monitoring of the state of health of RFBs. To accelerate the discovery of redoxmers with desired properties, state-of-the- art machine learning ( ML) methods, such as multiobjective Bayesian optimization (MBO), are useful. Here, we first employed density functional theory calculations to generate a database of reduction potentials, solvation free energies, and absorption wavelengths for 1400 redoxmer molecules based on a 2,1,3-benzothiadiazole (BzNSN) core structure. From the computed properties, we identified 22 Pareto-optimal molecules that represent best trade-off among all of the desired properties. We further utilized these data to develop and benchmark an MBO approach to identify candidates quickly and efficiently with multiple targeted properties. With MBO, optimal candidates from the 1400-molecule data set can be identified at least 15 times more efficiently compared to the brute force or random selection approach. Importantly, we utilized this approach for discovering promising redoxmers from an unseen database of 1 million BzNSN-based molecules, where we discovered 16 new Pareto-optimal molecules with significant improvements in properties over the initial 1400 molecules. Furthermore, we anticipate that this active learning technique is general and can be utilized for the discovery of any class of functional materials that satisfies multiple desired property criteria.

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

Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock

Computationally designed multi-subunit assemblies have shown considerable promise for a variety of applications, including a new generation of potent vaccines. One of the major routes to such materials is rigid body sequence-independent docking of cyclic oligomers into architectures with point group or lattice symmetries. Current methods for docking and designing such assemblies are tailored to specific classes of symmetry and are difficult to modify for novel applications. Here we describe RPXDock, a fast, flexible, and modular software package for sequence-independent rigid-body protein docking across a wide range of symmetric architectures that is easily customizable for further development. RPXDock uses an efficient hierarchical search and a residue-pair transform ( RPX ) scoring method to rapidly search through multidimensional docking space. We describe the structure of the software, provide practical guidelines for its use, and describe the available functionalities including a variety of score functions and filtering tools that can be used to guide and refine docking results towards desired configurations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structural comparison of allophycocyanin variants reveals the molecular basis for their spectral differences

Abstract Allophycocyanins are phycobiliproteins that absorb red light and transfer the energy to the reaction centers of oxygenic photosynthesis in cyanobacteria and red algae. Recently, it was shown that some allophycocyanins absorb far-red light and that one subset of these allophycocyanins, comprising subunits from the ApcD4 and ApcB3 subfamilies (FRL-AP), form helical nanotubes. The lowest energy absorbance maximum of the oligomeric ApcD4-ApcB3 complexes occurs at 709 nm, which is unlike allophycocyanin (AP; ApcA-ApcB) and allophycocyanin B (AP-B; ApcD-ApcB) trimers that absorb maximally at ~ 650 nm and ~ 670 nm, respectively. The molecular bases of the different spectra of AP variants are presently unclear. To address this, we structurally compared FRL-AP with AP and AP-B, performed spectroscopic analyses on FRL-AP, and leveraged computational approaches. We show that among AP variants, the α-subunit constrains pyrrole ring A of its phycocyanobilin chromophore to different extents, and the coplanarity of ring A with rings B and C sets a baseline for the absorbance maximum of the chromophore. Upon oligomerization, the α-chromophores of all AP variants exhibit a red shift of the absorbance maximum of ~ 25 to 30 nm and band narrowing. We exclude excitonic coupling in FRL-AP as the basis for this red shift and extend the results to discuss AP and AP-B. Instead, we attribute these spectral changes to a conformational alteration of pyrrole ring D, which becomes more coplanar with rings B and C upon oligomerization. This study expands the molecular understanding of light-harvesting attributes of phycobiliproteins and will aid in designing phycobiliproteins for biotechnological applications.

Gisriel, Christopher J.↗
DOE OSTI2023

Quantum Neural Networks: Issues, Training, and Applications

Our work in the field aims at explaining the limitations and expressive power of Quantum Machine Learning models, as well as finding feasible training algorithms that could be implemented in near-term Quantum Computers. The promise of Quantum Machine Learning is that by incorporating quantum effects, such as entanglement, into machine learning models researchers can improve model performance and understand more complex datasets. This pledge is particularly pronounced in the design of Quantum neural networks (QNNs), a promising framework for creating quantum algorithms, that promise to outperform classical models by combining the speedups of quantum computation with the widespread successes of deep learning. We show that applying this approach alone to quantum deep learning is problematic given that an excess of entanglement between the hidden and visible layers can destroy the predictive power of our QNN models. We address the barren plateau problem by suggesting the use of a generative, unbounded, nonlinear loss function with simple gradients. The loss function quantifies how much the quantum states generated by the QNNs differ from the data and the goal during training is to minimize it. Finally, we showcase how to use generative training to construct a "classical-quantum" neural network to accurately interpolate between the ground states of a Molecular Hamiltonian, a central question in Quantum Chemistry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Supramolecular Control of Ionic Retention in Electrolyte-Gated Synaptic Transistors

Electrolyte-gated transistors with ion-trapping layers offer a promising platform for artificial synapses in neuromorphic computing, yet molecular mechanisms governing ionic retention remain poorly understood. Here, in this study, we present a supramolecular approach to modulate ion retention by incorporating a crown ether derivative-based polymer network as an ion-trapping layer on top of a semiconducting monolayer. We show that the balance between ion–host binding and ion–solvent interactions dictates the kinetics of ion capture and release, which in turn controls the memory characteristics of the device. By varying the solvent dielectric constant, we tune the ionic retention time from nearly permanent trapping to rapid relaxation. Intermediate solvent polarity enables programmable short- and long-term synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, and long-term potentiation and depression. These findings establish a direct link between supramolecular ion recognition and synaptic plasticity and provide a generalizable design strategy for ionic–electronic neuromorphic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Two-dimensional parametric design of short pulse laser driven iron opacity experiments

High intensity short pulse lasers are commonly used to create high temperature and high density plasmas. These plasmas are used to study fundamental properties such as the equation of state and opacity. The targets often use small-diameter, thin buried layers of the materials of interest, surrounded by a tamper of low atomic number material such as plastic. Computational modeling is used to design and interpret experiments with short pulse lasers. Most of the modeling to date has assumed one-dimensional plane-parallel geometry. In this paper, the effects of radial gradients in the irradiation of thin planar targets are studied with one- and two-dimensional radiation/hydrodynamic simulations. It is found that the main effect of radial gradients is the averaging of plasma conditions and x-ray emission over the pattern of irradiation. Additionally, differences between one- and two-dimensional simulations arise because the plasma conditions and x-ray emission are, in general, nonlinear functions of the temperature of the plasma and thereby also nonlinear functions of the irradiation intensity. The differences increase with the ratio of the buried layer radius to the laser spot radius. The root mean square difference in the inferred iron L-shell opacity is less than 30% when the ratio is less than 1.0 and about 70% when the ratio is 1.5.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A Systematic, Polynomial-Cost Approach to Exact Correlation Energies (Final Technical Report)

The full configuration interaction (FCI) wave function provides the exact solution to the Schrödinger equation in a given basis set. While FCI is intractable due to exponential computational costs, the many-body expansion (or the method of increments) can reduce scaling to a low-order polynomial with system size. This project entailed advances in the incremental FCI (iFCI) approach, designed to allow iFCI to reach larger system sizes and maintain its intrinsic high accuracy. This document describes advances in solvers for iFCI, strategies to treat multiple charge and spin states, and virtual state management methods to reduce memory requirements. Overall, this project allows iFCI to correlate (for the first time) 142 valence electrons in 444 orbitals in a realistic model of a transition metal complex.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Enabling Efficient Water Splitting with Advanced Materials Designed for High pH Membrane Interface

This project was focused on developing the durable, high-performance materials and interfaces for advanced water splitting, enabling a clear pathway for achieving <$2/Kg H2 (on scale) with efficiency of 43 kWh/kg H 2 via anion exchange membrane (AEM)-based electrolysis. We aimed to advance these final goals via an improved fundamental understanding of both hydrogen and oxygen evolution reactions (HER/OER) leading to novel platinum group metal (PGM)-free catalyst materials in conjunction with critical improvements in membrane and ionomers and gas evolution electrodes with corresponding characterization and testing. Northeastern University (NU) lead this effort focusing on catalyst development and characterization (both in situ and ex situ) while project partners lead improvements in ionomer and membrane materials and will aid in the development of specialized electrode and membrane electrode assemblies. In addition, close collaboration occured with the HydroGEN Energy Materials Network (EMN) National Laboratory consortium including efforts related to use of advanced ionomers, durability protocols and validation of electrolyzer materials (e.g. NREL), multiscale modeling and computation (e.g. LBNL), and molecular dynamics (MD) simulations of the membrane catalyst interface (e.g. SNL). The interactions with HydroGEN included exchange of data and materials as needed to facilitate project success.

08 HYDROGEN↗
DOE OSTI2023

Structural Characterization of TRAF6 N-Terminal for Therapeutic Uses and Computational Studies on New Derivatives

Tumor necrosis factor receptor-associated factors (TRAFs) are a protein family with a wide variety of roles and binding partners. Among them, TRAF6, a ubiquitin ligase, possesses unique receptor binding specificity and shows diverse functions in immune system regulation, cellular signaling, central nervous system, and tumor formation. TRAF6 consists of an N-terminal Really Interesting New Gene (RING) domain, multiple zinc fingers, and a C-terminal TRAF domain. TRAF6 is an important therapeutic target for various disorders and structural studies of this protein are crucial for the development of next-generation therapeutics. Here, we presented a TRAF6 N-terminal structure determined at the Turkish light source “Turkish DeLight” to be 3.2 Å resolution at cryogenic temperature (PDB ID: 8HZ2). This structure offers insight into the domain organization and zinc-binding, which are critical for protein function. Since the RING domain and the zinc fingers are key targets for TRAF6 therapeutics, structural insights are crucial for future research. Separately, we rationally designed numerous new compounds and performed molecular docking studies using this template (PDB ID:8HZ2). According to the results, 10 new compounds formed key interactions with essential residues and zinc ion in the N-terminal region of TRAF6. Molecular dynamic (MD) simulations were performed for 300 ns to evaluate the stability of three docked complexes (compounds 256, 322, and 489). Compounds 256 and 489 was found to possess favorable bindings with TRAF6. These new compounds also showed moderate to good pharmacokinetic profiles, making them potential future drug candidates as TRAF6 inhibitors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Metal–organic frameworks as O 2 -selective adsorbents for air separations

Oxygen is a critical gas in numerous industries and is produced globally on a gigatonne scale, primarily through energy-intensive cryogenic distillation of air. The realization of large-scale adsorption-based air separations could enable a significant reduction in associated worldwide energy consumption and would constitute an important component of broader efforts to combat climate change. Certain small-scale air separations are carried out using N 2 -selective adsorbents, although the low capacities, poor selectivities, and high regeneration energies associated with these materials limit the extent of their usage. In contrast, the realization of O 2 -selective adsorbents may facilitate more widespread adoption of adsorptive air separations, which could enable the decentralization of O 2 production and utilization and advance new uses for O 2 . Here, we present a detailed evaluation of the potential of metal–organic frameworks (MOFs) to serve as O 2 -selective adsorbents for air separations. Drawing insights from biological and molecular systems that selectively bind O 2 , we survey the field of O 2 -selective MOFs, highlighting progress and identifying promising areas for future exploration. As a guide for further research, the importance of moving beyond the traditional evaluation of O 2 adsorption enthalpy, ΔH, is emphasized, and the free energy of O 2 adsorption, ΔG, is discussed as the key metric for understanding and predicting MOF performance under practical conditions. Based on a proof-of-concept assessment of O 2 binding carried out for eight different MOFs using experimentally derived capacities and thermodynamic parameters, we identify two existing materials and one proposed framework with nearly optimal ΔG values for operation under user-defined conditions. While enhancements are still needed in other material properties, the insights from the assessments herein serve as a guide for future materials design and evaluation. Computational approaches based on density functional theory with periodic boundary conditions are also discussed as complementary to experimental efforts, and new predictions enable identification of additional promising MOF systems for investigation.

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

Electronic Structure of the Au Nanoparticle-TiO 2 Heterojunction: Influence of Nanoparticle Size, Shape, Oxygen Vacancies, and Temperature

The electronic structure of the gold nanoparticle-titanium dioxide (AuNP-TiO 2 ) heterojunction plays a critical role for charge transfer and recombination dynamics that underpin its photocatalytic function. However, building a representative model to capture the key physics remains a significant challenge. Here, we investigate the influence of the AuNP size and shape, as well as oxygen vacancy (V O ) defects at the anatase-phase TiO 2 (101) surface and the temperature of the heterojunction, on its interfacial electronic properties. Using density functional theory (DFT), we compare the closed-shell Au 20 and open-shell Au 19 clusters interfaced with pristine and V O defect TiO 2 surfaces. We find that the presence of a V O defect transforms pure TiO 2 from a p-type to an n-type semiconductor, reversing the interfacial band bending from downward to upward. For the heterosystem, density of states (DOS) analysis shows that V O minimally affects the open-shell Au 19 -TiO 2 system, but it significantly alters the closed-shell Au 20 -TiO 2 system, converting it from p-type to n-type at the Γ-point at 0 K. Furthermore, ab initio molecular dynamics (AIMD) simulations at 300 K reveal significant thermal fluctuations in AuNP positions relative to the TiO 2 surface. These fluctuations result in dynamic variations in the gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) in all systems studied: Au 19 NP-Pristine TiO 2 , Au 20 NP-Pristine TiO 2 , Au 19 NP-V O defect TiO 2 , and Au 20 NP-V O defect TiO 2 . Complementary Bader charge analysis performed at both 0 K and finite-temperature AIMD snapshots supports the emergence of an upward band bending and the formation of a Schottky barrier at the V O containing heterojunctions. Notably, we find that at finite temperature, an Au atom can dynamically passivate the V O , leading to a pronounced widening of the HOMO-LUMO gap in the AuNP-V O defect TiO 2 heterojunctions. Our computational findings underscore the pivotal role of the V O defect and thermal effects in modulating interfacial band alignment, electronic states, and HOMO-LUMO gaps, providing insights for designing Au-TiO 2 heterojunctions with tailored electronic properties.

Mora Perez, Carlos [Lawrence Berkeley National Lab↗
DOE OSTI2022

Atomistic modeling of tensile deformation and fracture of carbon fibers: Nanoscale stress redistribution, effect of local structural characteristics and nanovoids

The mechanisms of tensile deformation and fracture of carbon fibers are investigated in large-scale molecular dynamics simulations. The simulations performed for computational samples with different structural characteristics enable analysis of the structural sensitivity of the mechanical properties. It is found that the carbon fiber nanostructure can be represented by a fine (nanoscale) mixture of stiff and soft regions. Despite the nanoscale structural heterogeneity of the computational fibers, their fracture strain and tensile strength exceed the experimental values by almost an order of magnitude. Here, this observation is explained by the ability of the carbon fiber nanostructure, largely consisting of curved and folded layers of turbostratic carbon, to exhibit continuous nanoscale load redistribution during the deformation. The transient, fluid nature of a percolating cluster of stress-holding structural elements prevents stress localization at the early stage of the deformation and enables the fiber nanostructure to adapt to the increasing load. Overall, the results of the simulations suggest that the design of advanced carbon fiber manufacturing and post-processing methods focused on the elimination of critical mesoscopic structural defects (internal voids, foreign inclusions, and surface flaws) can yield a substantial increase in the strength and fracture strain of carbon fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Generating forces in confinement via polymerization

Understanding how to produce forces using biomolecular building blocks is essential for the development of adaptive synthetic cells and living materials. Here we ask whether a dynamic polymer system can generate deformation forces in soft shells by pure self-assembly, motivated by the fact that biological polymer networks like the cytoskeleton can exert forces, move objects, and deform membranes by simply growing, even in the absence of molecular motors. We address this question by investigating polymer force generation by varying the release rate, the structure, and the interactions of self-assembling monomers. First, we develop a toy computational model of polymerization in a soft elastic shell that reveals the emergence of spontaneous bundling which enhances shell deformation. We then extend our model to account more explicitly for monomer binding dynamics. We find that the rate at which monomers are released into the interior of the shell is a crucial parameter for achieving deformation through polymer growth. Lastly, we demonstrate that the introduction of multivalent particles that can join polymers can either improve or impede polymer performance, depending on the amount and on the structure of the multivalent particles. Our results provide guidance for the experimental realization of polymer systems that can perform work at the nanoscale, for example through rationally designed self-assembling proteins or nucleic acids.

Osmanović, Dino [University of California at Los A↗
DOE OSTI2021

Learning curves for drug response prediction in cancer cell lines

Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness

One of the long-standing holy grails of molecular evolution has been the ability to predict an organism’s fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase’s fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Tailoring the cavities of hydrogen-bonded amphidynamic crystals using weak contacts: towards faster molecular machines

This work describes the use of C–H∙∙∙F–C contacts in the solid-state from the stator towards the rotator to fine-tune their internal motion, by constructing a set of interactions that generate close-fitting cavities in three supramolecular rotors 1–3I. The crystal structures of these rotors, determined by synchrotron radiation experiments at different temperatures, show the presence of such C–H∙∙∙F–C contacts between extended carbazole stators featuring fluorinated phenyl rings and the 1,4-diazabicyclo[2.2.2]octane (DABCO) rotator. According to the 2 H NMR results, using deuterated samples, and periodic density functional theory computations, the rotators experience fast angular displacements (preferentially 120° jumps) due to their low rotational activation energies (E a = 0.8–2.0 kcal mol -1 ). The higher rotational barrier for 1 (2.0 kcal mol -1 ) is associated with a larger number of weak C–H∙∙∙F–C contacts generated by the stators. This strategy offers the possibility to explore the correlation among weak intermolecular forces, cavity shape, and internal dynamics, which has strong implications in the design of future fine-tuned amphidynamic crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗