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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Tunable Cr 4+ Molecular Color Centers

The inherent atomistic precision of synthetic chemistry enables bottom-up structural control over quantum bits, or qubits, for quantum technologies. Tuning paramagnetic molecular qubits that feature optical-spin initialization and readout is a crucial step toward designing bespoke qubits for applications in quantum sensing, networking, and computing. In this work, we demonstrate that the electronic structure that enables optical-spin initialization and readout for S = 1, Cr(aryl) 4 , where aryl = 2,4-dimethylphenyl (1), o-tolyl (2), and 2,3-dimethylphenyl (3), is readily translated into Cr(alkyl) 4 compounds, where alkyl = 2,2,2-triphenylethyl (4), (trimethylsilyl)methyl (5), and cyclohexyl (6). The small ground state zero field splitting values (<5 GHz) for 1–6 allowed for coherent spin manipulation at X-band microwave frequency, enabling temperature-, concentration-, and orientation-dependent investigations of the spin dynamics. Electronic absorption and emission spectroscopy confirmed the desired electronic structures for 4–6, which exhibit photoluminescence from 897 to 923 nm, while theoretical calculations elucidated the varied bonding interactions of the aryl and alkyl Cr 4+ compounds. The combined experimental and theoretical comparison of Cr(aryl) 4 and Cr(alkyl) 4 systems illustrates the impact of the ligand field on both the ground state spin structure and excited state manifold, laying the groundwork for the design of structurally precise optically addressable molecular qubits.

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

A bistable and reconfigurable molecular system with encodable bonds

Molecular systems with ability to controllably transform between different conformations play pivotal roles in regulating biochemical functions. Here, we report the design of a bistable DNA origami four-way junction (DOJ) molecular system that adopts two distinct stable conformations with controllable reconfigurability by using conformation-controlled base stacking. Exquisite control over DOJ’s conformation and transformation is realized by programming the stacking bonds (quasi–blunt-ends) within the junction to induce prescribed coaxial stacking of neighboring junction arms. A specific DOJ conformation may be achieved by encoding the stacking bonds with binary stacking sequences based on thermodynamic calculations. Dynamic transformations of DOJ between various conformations are achieved by using specific environmental and molecular stimulations to reprogram the stacking codes. This work provides a useful platform for constructing self-assembled DNA nanostructures and nanomachines and insights for future design of artificial molecular systems with increasing complexity and reconfigurability.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Applying machine learning and quantum chemistry to predict the glass transition temperatures of polymers

Glass transition temperature (T g ) is important for understanding the physical and mechanical properties of a polymer material because it relates to the thermal energy required to transition between a hard glassy state and a soft rubbery one. Over the years, various models have been developed for predicting this thermal property from molecular structure to aid in designing novel polymers in selected classes. This work builds on those efforts by utilizing both machine learning (ML) and quantum chemistry (QC) techniques to develop models that can predict T g values from the molecular structure under different data availability scenarios and for a wide variety of polymer types. For the ML model, a graph convolutional network (GCN) was used to map topological polymer features; this model was trained against a dataset of more than 7500 T g values and resulted in a root mean square error (RMSE) of 38.1 °C. The QC-based regression model was trained on 83 T g values and produced an RMSE of 34.5 °C. In conclusion, this work demonstrated that while both model techniques produce accurate predictions and are suitable for different data availability scenarios, the QC-based regression model offered a more interpretable model framework with significantly less training data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Rejection of low-molecular weight neutral organics is highly sensitive to reverse osmosis system design and operation

A computational model was developed to investigate the significance of system design and operating conditions on the rejection of neutral, low-MW organics by reverse osmosis for potable reuse. Here, the model demonstrated that the decrease in local rejection as net driving pressure decreases is substantially greater for moderately rejected compounds than for highly rejected compounds. At recovery values less than 70%, the local permeate concentration can exceed the pressure vessel feed concentration for moderately rejected compounds. System-level rejection of moderately rejected compounds is likewise substantially more sensitive to operating conditions than highly rejected compounds. The findings highlight a drawback of relying on rejection results from bench-scale testing that operates at low recovery, which invariably has higher rejection than full-scale systems operating at similar pressure. The analysis demonstrates a trade-off in which the low-pressure, high-recovery operation desired for potable reuse systems can be detrimental to the removal of low-MW neutral organics. The removal of low-MW neutral organics can be improved if organics rejection is explicitly evaluated during the design process.

42 ENGINEERING↗
DOE OSTI2023

Balancing molecular level influences of intermolecular frustrated Lewis pairs (FLP) for successful design of FLP catalysts for hydrogen storage applications

The rise in energy demands and the deleterious environmental issues related to fossil fuels has led to a surge of interest in hydrogen as a "green" alternative. Hydrogen's extraordinary energy density makes it a potential energy and economic "power"-house. Significant research has been dedicated to materials-based hydrogen storage. One area, liquid organic hydrogen carriers (LOHC) is of substantial interest for the reversible transportation of hydrogen from production to end-use facilities. There are challenges associated with this technology including the dependency on precious metal-based catalysts. Recent work in frustrated Lewis pair (FLP) catalysis demonstrates promise for addressing these challenges. Here this review is focused on assessing recent literature on the utilization of intermolecular FLP main group catalysts for improved hydrogenation/dehydrogenation of various substrates including potential LOHC complexes. This review will present an overview of FLPs, highlight potential hydrogen storage applications, and propose areas where knowledge gaps exist that require further investigations.

08 HYDROGEN↗
DOE OSTI2024

Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole–dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. In conclusion, this study enables the ability to predict the structure–property relations for PAN and analogues with sustainable ab initio accuracy across scales.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computational design of mechanically coupled axle-rotor protein assemblies

Natural molecular machines contain protein components that undergo motion relative to each other. Designing such mechanically constrained nanoscale protein architectures with internal degrees of freedom is an outstanding challenge for computational protein design. Here we explore the de novo construction of protein machinery from designed axle and rotor components with internal cyclic or dihedral symmetry. We find that the axle-rotor systems assemble in vitro and in vivo as designed. Using cryo–electron microscopy, we find that these systems populate conformationally variable relative orientations reflecting the symmetry of the coupled components and the computationally designed interface energy landscape. These mechanical systems with internal degrees of freedom are a step toward the design of genetically encodable nanomachines.

Science & Technology - Other Topics↗
DOE OSTI2023

Active Causal Machine Learning for Molecular Property Prediction

Predicting properties from molecular structures is paramount to design tasks in medicine, materials science, and environmental management. However, design rules derived from the structure-property relationships using correlative data-driven methods fail to elucidate underlying causal mechanisms controlling chemical phenomena. This preliminary work proposes a workflow to actively learn robust cause-effect relations between structural features and molecular property for a broad chemical space utilizing smaller subsets, entailing partial information.

Fox, Zach↗
DOE OSTI2026

Rational design of heterogeneous single-site catalysts via surface organometallic chemistry

Single-site heterogeneous catalysts offer an attractive route to unite the molecular precision of homogeneous catalysis with the durability and practical advantages of solids. Surface organometallic chemistry (SOMC) provides a particularly powerful strategy for this purpose by grafting molecular precursors onto tailored surfaces and converting support functionalities into ligand environments for isolated metal centers. As a result, SOMC brings the language and logic of coordination chemistry to heterogeneous catalysis, where the support becomes an integral part of the active site coordination sphere. This Review surveys recent progress in the rational design of SOMC-derived single-site catalysts, with emphasis on synthetic routes, post synthetic transformations, and the deliberate tuning of catalytic behavior through metal-support interactions. Discussions are made on how support identity, hydroxyl topology, acidity, and redox activity shape the geometry, electronic structure, and oxidation state of supported metal sites, as well as how these factors determine activity, selectivity, and stability. We also examine a central limitation of these systems: despite their molecularly informed design, supported single sites often exist as structurally distributed ensembles rather than uniform species, particularly on amorphous supports. This site heterogeneity, along with catalyst dynamics under operating conditions, remains a major barrier to definitive structure-activity relationships. Therefore, emerging approaches that combine advanced characterization, first-principles modeling, ensemble kinetics, and machine learning to resolve active-site structure and guide catalyst development are highlighted. Together, these advances position SOMC as a versatile coordination chemistry framework for the predictive design of heterogeneous catalysts with well-defined molecularly tailored active sites.

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

Impact of direct exchange coupling via the insulator on the magnetic tunnel junction based molecular spintronics devices with competing molecule induced inter-electrode coupling

The magnetic tunnel junction (MTJ) based molecular spintronics device (MTJMSD) approach is suitable for mass production. This approach provides solutions to fabrication difficulties related to reliably connecting molecular device elements to the ferromagnets (FMs). To producing MTJMSD, the molecular channels are bridged across the insulator of an MTJ testbed with exposed side edges. In an MTJMSD, two FMs are simultaneously connected by an insulator film and the molecular channels along the exposed sides. In our prior experimental studies, we observed that molecules could produce strong coupling between ferromagnets in the presence of the competing coupling via an insulator. In this paper, our Monte Carlo Simulation (MCS) was used to study the impact of coupling variation via insulator (a.k.a. Ji) on the magnetic properties of an MTJMSD. We studied the effect of Ji while varying the molecule induced antiferromagnetic exchange coupling. The ferromagnetic or antiferromagnetic nature and magnitude of Ji determined the resultant effect. Antiferromagnetic Ji enhanced the pre-existing antiferromagnetic molecular coupling effect. Ferromagnetic Ji competed with the opposite nature of antiferromagnetic molecular coupling. Our MCS may help to understand the impact of insulator thickness and defects on the molecular spintronics device performance and design process.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fine‐Tuning Alkyl Chains on Quinoxaline Nonfullerene Acceptors Enables High‐Efficiency Ternary Organic Solar Cells with Optimizing Molecular Stacking and Reducing Energy Loss

Abstract Material design of guest acceptor is always a big challenge for improving the efficiency of ternary organic solar cells (OSCs). Here, a pair of isomeric nonfullerene acceptors based on quinoxaline core, Qx–p‐C 7 H 8 O and Qx–m‐C 7 H 8 O, is designed and synthesized. By moving the alkoxy chain attached on side phenyl frommeta‐position topara‐position, both π–π stacking distance and crystallinity are enhanced simultaneously. They obtain the uplifted lowest unoccupied molecular orbital level. Compared to Qx–m‐C 7 H 8 O, Qx–p‐C 7 H 8 O exhibits wider absorption spectrum and higher extinction coefficient. Using D18‐Cl:N3 as host materials, the addition of guest acceptor Qx–p‐C 7 H 8 O significantly improves the power conversion efficiency (PCE) from 17.61% to 18.49% because of higher open‐circuit voltage (0.875 V) and short‐circuit current density (27.85 mA cm −2 ). This can be attributed to the faster exciton dissociation, more balanced carrier mobility, fine fiber morphology, and lower energy loss in the ternary devices. However, Qx–m‐C 7 H 8 O‐based ternary device achieves relatively low PCE of 17.17% because this device shows extremely low electron mobility. The results indicate that molecular stacking, film morphology, etc., can be effectively modulated by fine‐tuning the side chains of guest materials, which may be an effective design rule for further improving the PCE of OSCs.

Chemistry↗
DOE OSTI2026

Restoring Symmetry and Enhancing Exchange via Chiral Molecular-Magnetic Hexagons

Modified behaviors of molecules, designed for device or energy applications, can occur due to lattice-molecule incompatibilities in point-group symmetries (PGS), long-range interactions between neighboring molecules, or latticemolecule charge transfer. Such sensitivities are prerequisite for the use of quantum molecules as devices, but removing certain broken symmetries is desirable from the standpoint of simplifying their behaviors. Here, we demonstrate symmetry restoration to molecular-magnetic lattices and show that it leads to strengthened exchange-coupling due to dipolar electrostatic interactions between neighboring molecules and due to manifestations of an earlier identification of a spin-sensitive slightly mobile electron by Hooshmand et al. Starting with a broken symmetry molecular magnet of current interest, we demonstrate a more robust structure composed of six molecular magnets on a h-BN-like surface. The broken point-group symmetry is healed by constructing interpenetrating equilateral triangular structures that are appropriately ratcheted to preserve three-fold rotational symmetry and inversion symmetry.

Pederson, Mark R.↗
DOE OSTI2021

Reinforcement learning in discrete action space applied to inverse defect design

Abstract Reinforcement learning (RL) algorithms that include Monte Carlo Tree Search (MCTS) have found tremendous success in computer games such as Go, Shiga and Chess. Such learning algorithms have demonstrated super-human capabilities in navigating through an exhaustive discrete action search space. Motivated by their success in computer games, we demonstrate that RL can be applied to inverse materials design problems. We deploy RL for a representative case of the optimal atomic scale inverse design of extended defects via rearrangement of chalcogen (e.g. S) vacancies in 2D transition metal dichalcogenides (e.g. MoS 2 ). These defect rearrangements and their dynamics are important from the perspective of tunable phase transition in 2D materials i.e. 2H (semi-conducting) to 1T (metallic) in MoS 2 . We demonstrate the ability of MCTS interfaced with a reactive molecular dynamics simulator to efficiently sample the defect phase space and perform inverse design—starting from randomly distributed S vacancies, the optimal defect rearrangement of defects corresponds a line defect of S vacancies. We compare MCTS performance with evolutionary optimization i.e. genetic algorithms and show that MCTS converges to a better optimal solution (lower objective) and in fewer evaluations compared to GA. We also comprehensively evaluate and discuss the effect of MCTS hyperparameters on the convergence to solution. Overall, our study demonstrates the effectives of using RL approaches that operate in discrete action space for inverse defect design problems.

42 ENGINEERING↗
DOE OSTI2021

Tailoring Interfaces in Solid-State Batteries Using Interfacial Thermochemistry and Band Alignment

Solid-state lithium ion batteries have enhanced thermal stability compared to batteries using liquid electrolytes. Although their practical implementation is limited by undesired side reactions between the electrode and electrolyte, different modification strategies have been proposed to stabilize these reactive interfaces. These approaches have been primarily based upon bulk materials properties, however, which may not necessarily be representative of the chemistry at the solid/solid interface. In this work, first principles calculations and X-ray scattering experiments are used to elucidate molecular-level reactivity and develop design principles for tailored interfaces based on surface and interfacial properties. Because of its well-known instability, the interface between the Li metal anode and the lithium lanthanum titanate (LLTO) solid electrolyte is applied as a model system. Density functional theory calculations are used to describe bulk, surface, and interfacial thermochemistry of the Li/LLTO system, and interfacial reconstruction is probed using ab initio molecular dynamics. These simulations of the Li/LLTO interface demonstrate facile decomposition concomitant with reduction of Ti 4+ ions. Based on further insights from computational analysis of the surface band edge positions, La 2 O 3 is proposed as an interlayer coating and is shown to provide an energetic barrier for interfacial charge transfer and decomposition reactions from X-ray reflectivity measurements and theoretical calculations. The findings in this work suggest that design strategies based on surface and interfacial properties can be used to kinetically stabilize electrode/electrolyte interfaces in solid-state batteries.

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

TATB thermal decomposition: Expanding the molecular profile with cryo-focused pyrolysis GC-MS

Understanding the molecular composition of high explosives during thermal decomposition is vital for predicting the sensitivity, safety, and performance of explosive materials. The thermal decomposition of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) has been linked to the formation of furazans through a series of dehydration reactions of the NO2 and NH2 groups on the phenyl ring, along with breakdown into small molecules (≤120 amu). Molecular identification of compounds formed in this transformation of the furazans to light gases has been lacking. To address this, we have applied a pseudo-confined sampling system in a cryo-focused pyrolysis gas chromatography-mass spectrometry (pyGC-MS) system to molecularly identify these intermediates. By design, sublimation of TATB, which has complicated MS analyses of thermal degradation, was significantly reduced and additional compounds were identified with potential structural information. In addition to the known furazan compounds, one of these compounds forms from the loss of oxygen from benzo-trifurazan (F3) and produces an open ring structure that may be the first step in the formation of lower molecular weight furazan breakdown products. The loss of a nitro group from benzo-monofurazan (F1) was also discovered and implicates the formation of oxidizing NO2 gas in the thermal decomposition mechanism. So these findings are vital for understanding the proper heat flow from energetic materials on a molecular level, necessary when measuring enthalpy and developing decomposition models based on kinetic parameters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗