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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
DOE OSTI2024

MolSym : A Python package for handling symmetry in molecular quantum chemistry

A consideration of the point group symmetry of molecules is often advantageous from a computational efficiency standpoint and sometimes necessary for the correct treatment of chemical physics problems. Many modern electronic structure software packages include a treatment of symmetry, but these are sometimes incomplete or unusable outside of that program’s environment. Therefore, we have developed the MolSym package for handling molecular symmetry and its associated functionalities to provide a platform for including symmetry in the implementation and development of other methods. Features include point group detection, molecule symmetrization, arbitrary generation of symmetry element sets and character tables, and symmetry adapted linear combinations of real spherical harmonic basis functions, Cartesian displacement coordinates, and internal coordinates. We present some of the advantages of using molecular symmetry as achieved by MolSym, particularly with respect to Hartree–Fock theory, and the reduction of finite difference displacements in gradient/Hessian computations. Furthermore, this package is designed to be easily integrated into other software development efforts and may be extended to further symmetry applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Data and scripts associated with “Non-random processes impacting organic matter chemistry are maximized in mid-order streams”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the publication “Non-random processes impacting organic matter chemistry are maximized in mid-order streams” submitted to Limnology and Oceanography (L&O) by Danczak et al. (in review). This package contains data and scripts used to investigate dissolved organic matter (DOM) molecular chemistry and diversification processes across 47 surface-water sampling sites in the Yakima River Basin, Washington, USA, during an August 2021 sampling campaign. The package contains analyses of ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), geochemical measurements, geospatial attributes, molecular diversity, and meta-metabolome ecological null models needed to reproduce the main manuscript results. The underlying field data were pulled from exising data packages at https://doi.org/10.15485/1892052 (Fulton et al., 2022) and https://doi.org/10.15485/1898914 (Grieger et al., 2022). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. We thank the following organizations for providing access to field locations for sample collection: the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, the Confederated Tribes and Bands of the Yakama Nation, and the Cowiche Canyon Conservatory. Research was conducted under Washington State Parks and Recreation Commission Scientific Research Permit #210901. We are grateful to the Yakama Nation Tribal Council and Yakama Nation Fisheries for their collaboration in facilitating sample collection and ensuring data usage aligns with their values and worldview. This data package contains an R-Markdown file for analyses and five folders: (1) Data, (2) Geospatial Data, (3) Supplemental_Files, (5) Figures_pdf, (4) and src. The Data folder contains tabular inputs and derived files used in the manuscript analysis. The Geospatial Data folder contains climate and water-balance, hydrologic, land-cover, population/regional water-use, stream, topographic, and stream-order attribute CSV files. The src folder contains scripts used to process data, run analyses, and generate figures. The Figures_pdf folder contains manuscript figure outputs. The Supplemental_Files folder contains supplemental analysis products. All files are .csv, .pdf, .html, .png, .R, .Rmd, .svg, or .tre. This data package is associated with the rcfsa-RC2-SPS_Null_Modeling repository found at https://github.com/river-corridors-sfa/rcfsa-RC2-SPS_Null_Modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Optical interference effect in strong-field electronic coherence spectroscopy

Here, we investigate strong-field-induced electronic coherences in argon and molecular nitrogen ions created by high-intensity, few-cycle infrared laser pulses. This is a step toward the long-sought goal of strong-field coherent control in molecular chemistry. We employ high-intensity, few-cycle infrared laser pulses in a pump-probe setup to investigate a recent prediction that electronic coherences in nitrogen molecules change the ion yields versus pump-probe delay. [Yuen et al., Phys. Rev. A 109, L011101 (2024)]. The predicted coherence signals in molecular nitrogen could not be resolved above the optical interference of the pump and probe pulses; a simultaneous measurement clearly resolved the induced cation fine-structure coherence in strong-field-ionized argon. The results of our comparison with simulations suggest that optical interference effects manifest differently in each ionic species and must be carefully accounted for when interpreting experimental data. We find that nonsequential double ionization in the low-intensity region of the focal volume can reduce the visibility of coherence generated by two-pulse sequential ionization, and we quantify the importance of pulse shape and spectral characteristics for isolating the desired coherence signals.

charge migration↗
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 OSTI2026

A physically based mechanical model for Mullins effect in thermoplastic polyurethanes

Despite decades of research, connecting the chemical and physical structure of thermoplastic polyurethanes to their mechanical properties remains highly challenging. Of particular note are their large-deformation and rate-dependent behaviors, which vary greatly with molecular chemistry, including the type and relative content of soft and hard segments. In this work, we develop a physically motivated mechanical theory for predicting the behavior of thermoplastic polyurethanes. The theory incorporates a representation of microstructural evolution during mechanical deformation, which captures the signatures of stress softening over cyclic loading (commonly referred to as the Mullins effect). There are only eight physically motivated fitting parameters, including a direct dependence on the hard segment fraction. The model predicts that increasing the hard segment fraction leads to higher stiffness and greater energy dissipation, in quantitative agreement with published experimental data. Furthermore, we provide a comprehensive analysis of the model and validate its predictions across several independent datasets focused on mechanical characterization. Direct comparisons to experimental data demonstrate its predictive capability on the effect of loading rate, cyclic deformations, and applied tension or compression. Altogether, this work establishes a predictive framework that connects polymer chemistry and microstructure to emergent mechanical behaviors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spin Glass Behavior and Giant Magnetoresistance via Aliovalent Fe/Ni Alloying in Amorphous Tetrathiafulvalene-Tetrathiolate Coordination Polymers

The discovery of materials with programmable combinations of charge transport and magnetic properties is a major goal in synthetic chemistry. Molecular materials, such as coordination polymers (CPs), are emerging candidates in this field due to their synthetic modularity. While there are many conductive or magnetic CPs, single-component examples with both properties are still emerging. Here, in this work, we demonstrate how alloying paramagnetic Fe(III) centers into a highly conductive Ni-based CP results in novel amorphous materials with high conductivity and giant magnetoresistance. Aliovalent doping with paramagnetic Fe centers engenders spin-glass transitions, while Ni and the strong π-stacking interactions of tetrathiafulvalene-2,3,6,7-tetrathiolate (TTFtt) ligands support conductivity. Ni 0.69 Fe 0.31 TTFtt has σ ≈ 200 S/cm and a 1.8 K magnetoresistance of −52% at 5 T, among the largest for any coordination solid. This work demonstrates not only how magnetic properties can be rationally incorporated into conductive CPs, but also an unexpected potential for amorphous materials in spintronic applications.

amorphous materials↗
DOE OSTI2026

High-Energy Hybridized States Enable Long-Lived Hot Electrons in Cobaloxime-Silicon Nanocrystal System

Strong electronic coupling is achieved between the molecular catalyst cobaloxime ([Co]) and silicon nanocrystals (Si NCs) bridged by an ethylenepyridine group derived from vinylpyridine (vpy) covalently bound to the Si NC surface (Si-vpy-[Co]). The ethylenepyridine tether in Si-vpy-[Co] is key to dramatic changes to the system’s physical properties which are not observed in the corresponding formylpyridine (fpy) system (Si-fpy-[Co]) consistent with strong electronic coupling previously observed only in dark electrochemical systems. UV−vis absorption spectroscopy reveals new [Co]-centered electronic states in Si-vpy-[Co], and transient absorption spectroscopy finds a strong absorption feature appearing within 250 fs and persisting for at least 5 ns. Astoundingly, spectroelectrochemical measurements reveal that this absorption feature is consistent with both the singly reduced [Co] − and doubly reduced [Co] 2− complexes, leading to the conclusion that these long-lived charges are derived from high-energy “hot” electrons residing in [Co]-centered states. Detailed analysis using cyclic voltammetry, spectroelectrochemistry, electron paramagnetic resonance spectroscopy, and density functional theory (DFT) calculations provides insight into the unique electronic structure created in Si-vpy-[Co]. DFT reveals that the new electronic states arise from hybridization between deep Si NC band states and high-energy molecular orbitals of the ethylenepyridine tether and the [Co] catalyst and are facilitated by σ-bonding character at the ethylenepyridine linkage. This study demonstrates that strong electronic coupling achieved through precise molecular chemistry can change the paradigm of otherwise fixed energy levels in hybrid photoelectrochemical systems for artificial photosynthesis and related applications.

14 SOLAR ENERGY↗
DOE OSTI2025

Contribution to Open Molecules Dataset (coordcomplexsampling)

The Open Molecules Dataset is a project led by external collaborators, focused on the production of a wide diversity of molecular chemistries. This project will include high-throughput electronic structure calculations on metal coordination complexes, biomolecules, and electrolytes. Solvation effects, conformers, chemical reactivity, and spin/charge sampling will be pursued for elements on the periodic table up to, but not including the actinides. We aim to contribute code related to sampling for transition metal complexes, in particular, and expand software capabilities related to other thrusts desired.

Taylor, Michael G. [Los Alamos National Laboratory↗
DOE OSTI2025

An Unconventional Dark Radical Chemistry in Dense Molecular Clouds: Directed Gas-Phase Formation of Naphthyl Radicals

The synthetic pathways to aromatic molecules inside photon-shielded dense molecular clouds remain a fundamental, unsolved enigma in astrochemistry and astrophysics, with low-temperature molecular growth routes involving aromatic radicals, such as prototype bicyclic naphthyl (C 10 H 7 • ), implicated as key sources. Here, exploiting crossed molecular beam experiments augmented by electronic structure calculations, unexpected pathways are exposed leading to the gas-phase formation of 1- and 2-naphthyl via barrierless bimolecular reactions of atomic carbon (C) with indene (C 9 H 8 ) and of dicarbon (C 2 ) with styrene (C 8 H 8 ) accompanied by ring expansion and cyclization together with aromatization. These facile routes challenge conventional wisdom that aromatic radicals are formed in deep space solely via “bright” gas-phase photochemistry of their closed-shell polycyclic aromatic hydrocarbon (PAH) precursors. A hitherto disregarded “dark” aromatic radical chemistry with aromatic radicals synthesized via gas-phase reactions offers new concepts on the chemical evolution of the chemistry of dark molecular clouds eventually culminating in the rapid formation of aromatics, fullerenes, and carbonaceous nanostructures.

Aromatic compounds↗
DOE OSTI2024

Evidence of both molecular cloud and fluid chemistry in Ryugu regolith

The sulfur chemistry of (162173) Ryugu particles can be a powerful tracer of molecular cloud chemistry and small body processes, but it has not been well explored. We report identification of organosulfurs and a sulfate grain in two Ryugu particles, A0070 and A0093. The sulfate grain shows oxygen isotope ratios (δ 17 O = -11.0 ± 4.3 per mil, δ 18 O = -7.8 ± 2.3 per mil) that are akin to silicates in Ryugu but exhibit mass-independent sulfur isotopic fractionation (Δ 33 S = +5 ± 2 per mil). A methionine-like coating on the sulfate grain is isotopically anomalous (δ 15 N = +62 ± 2 per mil). Both the sulfate and organosulfurs can simultaneously form and survive during aqueous alteration within Ryugu’s parent body, under reduced conditions, low temperature, and a pH >7 in the presence of N-rich organic molecules. This work extends the heliocentric zone where anomalous sulfur, formed by selective photodissociation of H 2 S gas in the molecular cloud, is found.

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

Unravelling the Glycan Code: Molecular Dynamics and Quantum Chemistry Reveal How O-Glycan Functional Groups Govern OgpA Selectivity in Mucin Degradation by Akkermansia muciniphila

Mucins, heavily O-glycosylated glycoproteins, are a key component of mucus, and certain gut microbiota, including Akkermansia muciniphila , can utilise mucin glycans as a carbon source. Akkermansia muciniphila produces the O-glycopeptidase enzyme OgpA, which cleaves peptide bonds at the N-terminus of serine (Ser) or threonine (Thr) residues carrying O-glycan substitutions, with selectivity influenced by the O-glycan functional groups. Using molecular dynamics (MD) simulations and quantum chemistry calculations, we explored how different O-glycan groups affect OgpA's selectivity. Our results show that peptides bind to the enzyme via hydrogen bonds, π–π interactions, van der Waals forces and electrostatic interactions, with key residues, including Tyr90, Val138, Gly176, Tyr210 and Glu91, playing important roles. The primary determinant of selectivity is the interaction between the peptide's functional group and the enzyme's binding cavity, while peptide–enzyme interface interactions are secondary. Quantum chemistry calculations reveal that OgpA prefers peptides with a lower electrophilic character. This study provides new insights into mucin degradation by gut microbiota enzymes, advancing our understanding of this critical biological process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Aluminum Based Solvent-Free Organic–Inorganic Hybrid Materials

In emerging materials, molecular hybrids are especially promising, as they have molecular level mixing of the organic and inorganic components, producing homogeneous materials without interfaces that can deteriorate properties. However, the current methods of manufacturing molecular hybrids are based on solution processing, which is impractical for bulk materials such as may be used for optically clear radiation and electromagnetic shielding components or photonics. Here we examine molecular hybrids composed of aluminum isopropoxide (AIP) and epoxy resins aiming to understand the molecular scale chemistry and manufacturability of these hybrid materials. DSCmonitored cure revealed the ideal cure temperature for these materials is 160−170 °C and demonstrated that an AIP concentration of 16.7 wt % maximizes the extent of reaction. Kinetic analysis of the curing reaction showed the Sestak−Berggren autocatalytic model is effective at temperatures over 140 °C but the reaction has diffusion limitations at a temperature of 120 °C. Mechanical testing with custom resin molds revealed a decrease in properties of the bulk samples with increasing AIP content due to an increase in defects but further testing with nanoindentation demonstrated comparable or improved mechanical properties of AIP-epoxy hybrids compared to epoxy resin with a standard hardener. Ultimately, this work lays the foundation for hardener-free epoxy-aluminum inorganic/organic hybrids and presents opportunities to expand on properties for specific applications such as thermal conductivity, optical clarity, and dielectric constant.

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

A cavity-enhanced spectroscopist's lens on molecular polaritons

Polariton chemistry has been hailed as a potential new route to direct molecular processes with electromagnetic fields. To make further strides, the community needs to clarify which unusual polaritonic phenomena are true hallmarks of cavity quantum electrodynamics and which can be rationalized with classical optical physics. Here, in this work, we provide a tutorial perspective on the formation, spectroscopy, and behavior of molecular polaritons using classical optics. Where possible, we draw connections to cavity-enhanced spectroscopy and recast open questions in terms that may be more familiar to the broader community of physical chemists.

Cavity quantum electrodynamics↗
DOE OSTI2026

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential

A molecular-level understanding of electrolyte solvation structure and ion–ion correlations is critical to developing next-generation battery chemistries. Atomistic simulation capabilities with sufficient accuracy, speed, and transferability to deliver reliable structural insights while avoiding arduous system-specific reparameterization are thus highly desirable. Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse data sets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000× faster than DFT. While previous MLIP training data sets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) data set provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pretrained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation–solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations beyond the limits of classical force fields and direct DFT molecular dynamics, serving as a powerful tool for accelerating the design of next-generation Na-ion battery electrolytes and beyond.

MLIPs↗
DOE OSTI2024

CACTUS: Chemistry Agent Connecting Tool Usage to Science

Large language models (LLMs) have shown remarkable potential in various domains but often lack the ability to access and reason over domain-specific knowledge and tools. In this article, we introduce Chemistry Agent Connecting Tool-Usage to Science (CACTUS), an LLM-based agent that integrates existing cheminformatics tools to enable accurate and advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b, Mistral-7b, and Llama3-8b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without a significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with widely used domain-specific tools provided by RDKit, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗