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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Conserved Macromolecular Architecture of Poplar Secondary Cell Walls Revealed by ssNMR and Atomistic Modeling

The macromolecular architecture of plant secondary cell walls governs wood's mechanical and biochemical properties, yet its natural intra-species variability remains poorly characterized. Here, we combined 13C solid-state NMR (ssNMR), multivariate statistical analysis, and molecular modeling to profile nanoscale structure across 13 genetically diverse Populus trichocarpa genotypes grown in 13C-enriched atmospheres. SsNMR-derived phenotypes spanning composition, structure, mobility, and inter-polymer proximities reveal a conserved architecture, with a subtle yet coordinated variation organizing into dominant structural and secondary mobility axes. A representative atomistic model captures these features and reproduces experimental metrics. Molecular dynamics simulations support a weak but consistent positive correlation between cellulose abundance and crystalline-like order, with interior cellulose chains enriched in tg (trans-gauche) conformations without expanding crystalline cores. Together, experiment and simulation reveal a genetically buffered, broadly conserved nanoscale architecture across genotypes, where subtle fine-tuning of cellulose bundling and matrix packing balances mechanical performance with biological function.

09 BIOMASS FUELS↗
DOE OSTI2024

Alternative CNDOL Fockians for fast and accurate description of molecular exciton properties

CNDOL is an a priori, approximate Fockian for molecular wave functions. In this study, we employ several modes of singly excited configuration interaction (CIS) to model molecular excitation properties by using four combinations of the one electron operator terms. Those options are compared to the experimental and theoretical data for a carefully selected set of molecules. The resulting excitons are represented by CIS wave functions that encompass all valence electrons in the system for each excited state energy. The Coulomb–exchange term associated to the calculated excitation energies is rationalized to evaluate theoretical exciton binding energies. This property is shown to be useful for discriminating the charge donation ability of molecular and supermolecular systems. Multielectronic 3D maps of exciton formal charges are showcased, demonstrating the applicability of these approximate wave functions for modeling properties of large molecules and clusters at nanoscales. This modeling proves useful in designing molecular photovoltaic devices. Our methodology holds potential applications in systematic evaluations of such systems and the development of fundamental artificial intelligence databases for predicting related properties.

Chemistry↗
DOE OSTI2025

Key Factors in Semi-Generic Coarse-Grained Modeling of Solid Polymer Electrolytes

Given the long length and time scales of interest and strong interactions present in solid polymer electrolytes, coarse-grained molecular models can be useful in understanding the molecular basis of their structure and transport properties. Rather than representing atoms individually, coarse-grained models use beads representing groups of atoms, making the system simpler and more efficient. Generic coarse-grained models, that are not built based on matching a particular atomistic system, can provide insight into the important physical considerations that apply across different chemical systems, though it can be unclear how to best match to experimental systems and capture relevant experimental behaviors with as simple of a model as possible. Here, we build on generic bead-spring models, but use stiff angle potentials, set different bead properties for different polymer types, and include additional ion parameters, creating a semi-generic coarse-grained model that can map more specifically to polymers and copolymers with different chain architectures, component glass transition temperatures, and ion solvation behaviors. We set most parameters with basic homopolymer data such as glass transition temperatures, Kuhn length, density, and dielectric constant, with further adjustment based on data from the polymer electrolyte system. We specifically model polystyrene-block-poly(oligo-oxyethylene methyl ether methacrylate) (PS-b-POEM) with lithium triflate salt, considering the POEM to be made of a poly(methylmethacrylate) backbone with poly(ethylene oxide) side chains. Solvation of ions is accounted for by additional polymer-ion interactions of the form -S/r4, plus additional lithium-polymer Lennard-Jones interactions. We find these potentials have different effects and discuss strategies for setting these parameters.

Zhang, Yuanhao↗
DOE OSTI2024

High throughput, accurate gene annotation through AI and HPC-enabled structural analysis

With the advances in next generation sequencing technologies, the number of sequenced genomes is growing exponentially, resulting in a technology bottleneck for the translation of sequence information into usable hypotheses about the function of each gene. We have proposed leveraging our leadership high-performance computing (HPC) resources to help break this annotation bottleneck. Here we design an HPC-based framework to infer gene function from gene sequence by incorporating information about protein structure and interactions predicted by deep learning approaches. Accurate functional prediction and gene annotation using computational methods will facilitate breakthroughs in the genomic sciences essential to understanding and harnessing life processes in bacteria, fungi and plants. The development and applications of the state-of-the-art deep neural networks to protein structural modeling, interaction prediction, sequence comparison, and quality assessment of protein structural models will be made possible by leadership computational resources. These HPC-enabled bioinformatics and molecular modeling tools will provide powerful insights into molecular functions of genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A fully ro-vibrationally resolved corona model for the molecular hydrogen Fulcher-𝛼 system

A ro-vibrationally resolved corona model for the molecular hydrogen Fulcher-𝛼 transition based on the Yacora solver is introduced. The model couples 1365 ro-vibrational levels of the 𝑋 1 ⁢𝛴$^+_𝑔$, 𝑑 3⁢ 𝛱 𝑢 and 𝑎 3 ⁢𝛴$^+_𝑔$ states via electron impact excitation from the ground state into the 𝑑 3⁢ 𝛱 𝑢 state and subsequent spontaneous emission into the 𝑎 3 ⁢𝛴$+_𝑔$ state. For the process of electron impact excitation a set of 45260 fully ro-vibrationally resolved cross sections calculated with the molecular convergent close-coupling (MCCC) method in the adiabatic-nuclei formulation is applied. The MCCC cross sections are compared with results of a scaling method used in other works, demonstrating the need for dedicated ro-vibrationally resolved cross sections. By post-processing the model output, entire Fulcher spectra can be simulated. These spectra are benchmarked with measurements from an inductively coupled plasma discharge at a pressure between 1.1–10 Pa and a RF power of 700 W. The model results agree very well with the experiment both in relative shape and in absolute value. A non-ro-vibrationally resolved, purely electronically resolved collisional radiative model for molecular hydrogen (including further states and reaction channels) is applied to investigate the validity of the corona approximation for the benchmark plasmas and the relevance of possible process extensions of the corona model. Furthermore, the influence of collisional quenching, autoionization and predissociation is discussed.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

InteraChem: Virtual Reality Visualizer for Reactive Interactive Molecular Dynamics

Interactive molecular dynamics in virtual reality (IMD-VR) simulations provide a digital molecular playground for students as an alternative or complement to traditional molecular modeling kits or 2D illustrations. Previous IMD-VR studies have used molecular mechanics to enable simulations of macromolecules such as proteins and nanostructures for the classroom setting with considerable success. Here, we present the InteraChem molecular visualizer, intended for reactive IMD-VR simulation using semiempirical and ab initio methods. InteraChem visualizes not only the molecular geometry but also (1) isosurfaces such as molecular orbitals and electrostatic potentials and (2) two-dimensional graphs of time-varying simulation quantities such as kinetic/potential energy, internal coordinates, and user-applied force. Additionally, InteraChem employs speech recognition to facilitate user interaction and introduces a novel “atom happiness” visualization using emojis to indicate the energetic feasibility of a particular bonding arrangement. Furthermore, we include a set of accompanying exercises that we have used to teach chemical reactivity in small molecular systems.

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

Characterization and molecular simulation of lignin in Cyrene pretreatment of switchgrass

Biomass-derived solvents have been proposed as a novel pathway in biorefining for the realization of biofuels and bioproducts derived from lignocellulosic biomass. Cyrene derived from cellulose has recently been shown to have a high potential as a green organic solvent for pretreating poplar biomass. However, due to its high dynamic viscosity nature, high Cyrene concentration could cause negative effects on the sugar release of the pretreated biomass as well as driving up the operational cost of the lignin recovery. In this study, we combine experimental and computational approaches to examine the impact of Cyrene pretreatment with reduced Cyrene concentration under mild conditions on switchgrass lignin. Our experimental studies indicated correlation between pretreatment condition and recovery and structure modification of lignin. Switchgrass lignin extracted by Cyrene pretreatment possessed high preservation of β-O-4 ether inter-unit linkage, which could provide versatility in the integration of downstream lignin valorization into the modern biorefinery industries. Molecular modeling examining the solvation of switchgrass lignin polymer and the disaggregation of low-molecular weight lignin aggregates under pretreatment conditions indicated that a preferential interaction exists between Cyrene and lignin, which likely drives lignin release, and that the disruption of inter-lignin contacts can be modulated as a non-monotonic function of Cyrene : water ratio. Further, while Cyrene–lignin interactions permit the solubilization of lignin, simulations with proxy reactive-species reveal that changes to the diffusion of these reactive proxies and their localization near linkage sites under Cyrene conditions may inhibit chemical processes. In conclusion, the results indicated that loss of pretreatment efficacy caused by low Cyrene concentration could be compensated by prolonged pretreatment time and high catalyst dosage.

09 BIOMASS FUELS↗
DOE OSTI2020

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (similar to 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. In this work, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (similar to 1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with similar to 500 total reference calculations. Using an extensive DFT test set of similar to 1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/angstrom of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

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

Molecular Dynamics Simulations of a Catalytic Multivalent Peptide–Nanoparticle Complex

Molecular modeling of a supramolecular catalytic system is conducted resulting from the assembling between a small peptide and the surface of cationic self-assembled monolayers on gold nanoparticles, through a multiscale iterative approach including atomistic force field development, flexible docking with Brownian Dynamics and µs-long Molecular Dynamics simulations. Self-assembly is a prerequisite for the catalysis, since the catalytic peptides do not display any activity in the absence of the gold nanocluster. Atomistic simulations reveal details of the association dynamics as regulated by defined conformational changes of the peptide due to peptide length and sequence. Our results show the importance of a rational design of the peptide to enhance the catalytic activity of peptide–nanoparticle conjugates and present a viable computational approach toward the design of enzyme mimics having a complex structure–function relationship, for technological and nanomedical applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Isolation of a Terminal Cobalt Nitride in a Metal–Organic Framework

Transition metal nitrides are reactive intermediates in biological and industrial processes. Chemists have synthesized molecular model complexes of such reactive species to understand their function and electronic requirements for new applications. However, molecular chemistry can suffer from intra- and intermolecular decomposition pathways, which preclude further discovery of unknown reactive species. Metal-organic frameworks offer an opportunity for creating long-lived forms of such species with the vacuum of the pore suppressing degradation while simultaneously enabling substrate access for controlled reactivity studies. Here, in this study, we report the characterization of an elusive terminal cobalt nitride species generated through photolysis or thermolysis of a site-isolated cobalt azide within the evacuated metal-organic framework CoN 3 -MFU-4l. The first crystal structure of such a species is presented, with vibrational, X-ray absorption, and electron paramagnetic resonance spectroscopies providing further direct evidence for its formation while elucidating its electronic structure. The system additionally enables subsequent reactivity studies with selected substrates, revealing a unique ambiphilic behavior for a metal nitride species.

Börgel, Jonas [University of California, Berkeley,↗
DOE OSTI2023

Surface Chemistry and Reactions for Bimetallic Au Catalysis (Final Report)

Gold-based catalysts offer tremendous opportunities for developing new technologies for selective oxidation reactions that directly use gas-phase oxygen as a benign reagent and produce either no byproducts or water as the only byproduct. These processes are urgently needed and critical for achieving the sustainable production of chemicals. While recent studies demonstrate that the catalytic properties of gold can be significantly enhanced by addition of other metals, the development of these catalysts with sufficient performance for commercialization is hindered by a lack of fundamental understanding of how the catalytic properties of gold can be controlled and tailored. This project addressed this need by synergistically combining experimental and computational studies to establish fundamental composition-structure-activity relationships for gold-based catalysts for several industrially important reactions. Results from surface spectroscopy, atomic imaging and thermal desorption experiments were combined with molecular models and reaction mechanisms from quantum chemical calculations to link observable reaction rates to the structure and composition of surfaces at the molecular level. The results were used to develop improved catalyst formulations and identify optimized reaction conditions for gold-based catalysts. For broader impacts, the developed methodology was extended to other metallic catalysts, such as silver, platinum-molybdenum and nickel-tin, for improved catalytic efficiency and sustainable production of specialty and commodity chemicals.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Octupole moment driven free charge generation in partially chlorinated subphthalocyanine for planar heterojunction organic photodetectors

In this study, high-performance organic photodetectors are presented which utilize a pristine chlorinated subphthalocyanine photoactive layer. Optical and optoelectronic analyses indicate that the device photocurrent is primarily generated through direct charge generation within the chlorinated subphthalocyanine layer, rather than exciton separation at layer interfaces. Molecular modelling suggests that this direct charge generation is facilitated by chlorinated subphthalocyanine high octupole moment (-80 DÅ 2 ), which generates a 200 meV shift in molecular energetics. Increasing the thickness of chlorinated subphthalocyanine leads to faster response time, correlated with a decrease in trap density. Notably, photodetectors with a 50 nm thick chlorinated subphthalocyanine photoactive layer exhibit detectivities approaching 10 13 Jones, with a dark current below 10 -7 A cm -2 up to -5 V. Based on these findings, we conclude that high octupole moment molecular semiconductors are promising materials for high-performance organic photodetectors employing single-component photoactive layer.

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

Planar, curved and twisted molecular nanographenes: Reduction-induced alkali metal coordination

Planar and curved polycyclic aromatic hydrocarbons (PAHs) attract significant attention as molecular models of fullerenes, carbon nanotubes, and graphene, thus stimulating broad investigation of chemical reactivity and materials applications of designed nanocarbon π-systems. Non-planar molecular nanographenes (NGs) recently emerge as advanced anode materials in energy storage, showing high reduction limits and enhanced alkali metal intercalation levels. However, the lack of direct structure–property correlations in such complex hybrid systems impedes their further development and utilization. With a focus on alkali-metal-induced reduction of selected PAHs, we herein review original metal binding and intercalation trends, site specific coordination, and distinct carbon framework responses to stepwise electron uptake. Small planar graphene fragments, like triphenylene and coronene, are compared to π-expanded hexabenzocoronenes, followed by the discussion of bowl-shaped corannulene, sumanene and other carbon bowls, as well as bent, warped, and twisted molecular nanographenes. The effect of size, symmetry, and framework topology along with the structural deformation of carbon backbones upon reduction are analyzed, using recent crystallographic examples of alkali-metal intercalated products. In conclusion, the revealed insights into the structures, binding, and metal intercalation in molecular nanographenes should stimulate their future applications as new functional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗