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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 OSTI2026

Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion Pairing

Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving the training and computational efficiency of machine-learned interatomic potentials. We demonstrate the framework on Ca 2+ –CO 3 2– ion pairing in aqueous solution, a key process underlying CO 2 mineralization in seawater. As proof of principle, we first show that fine-tuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. The computed ion-pair association free energy is in quantitative agreement with experimental measurements, further validating the accuracy of the ECW-TL framework. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to efficient simulations of complex aqueous and interfacial chemical processes.

cluster chemistry↗
DOE OSTI2024

Effect of layer bending on montmorillonite hydration and structure from molecular simulation

Conceptual models of smectite hydration include planar (flat) clay layers that undergo stepwise expansion as successive monolayers of water molecules fill the interlayer regions. However, X-ray diffraction (XRD) studies indicate the presence of interstratified hydration states, suggesting non-uniform interlayer hydration in smectites. Additionally, recent theoretical studies have shown that clay layers can adopt bent configurations over nanometer-scale lateral dimensions with minimal effect on mechanical properties. Therefore, in this study we used molecular simulations to evaluate structural properties and water adsorption isotherms for montmorillonite models composed of bent clay layers in mixed hydration states. Results are compared with models consisting of planar clay layers with interstratified hydration states (e.g. 1W–2W). The small degree of bending in these models (up to 1.5 Å of vertical displacement over a 1.3 nm lateral dimension) had little or no effect on bond lengths and angle distributions within the clay layers. Except for models that included dry states, porosities and simulated water adsorption isotherms were nearly identical for bent or flat clay layers with the same averaged layer spacing. Similar agreement was seen with Na- and Ca-exchanged clays. In conclusion, while the small bent models did not retain their configurations during unconstrained molecular dynamics simulation with flexible clay layers, we show that bent structures are stable at much larger length scales by simulating a 41.6×7.1 nm 2 system that included dehydrated and hydrated regions in the same interlayer.

58 GEOSCIENCES↗
DOE OSTI2025

Wettability of Two-Dimensional Carbon Allotropes from Molecular Simulations

Force-field Monte Carlo and Molecular Dynamics simulations are used to compare wetting behaviors of model carbon sheets mimicking neat graphene, its saturated derivative, graphane, and related planar allotropes penta-graphene, γ-graphyne, and ψ-graphene in contact with aqueous droplets or an aqueous film confined between parallel carbon sheets. Atomistic and area-integrated surface/water potentials are found to be essentially equivalent in capturing moderate differences between the wetting free energies of tested substrates. Despite notable differences in mechanical and electric properties of distinct allotropes, the predicted allotrope/water contact angles span a narrow window of weakly hydrophilic values. Contact angles in the range of 80 ± 10° indicate modest hydration repulsion incapable of competing with van der Waals attraction between carbon particles. Poor dispersibility in neat water is hence a common feature of studied materials.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Depletion of the Protein Hydration Shell with Increasing Temperature Observed by Small-Angle X-ray Scattering and Molecular Simulations

The hydration shell is an integral part of proteins since it plays key roles in conformational transitions, molecular recognition, and enzymatic activity. While the dynamics of the hydration shell have been described by spectroscopic techniques, the structure of the hydration shell remains less understood due to the lack of hydration shell-sensitive structural probes with high spatial resolution. We combined temperature-ramp small-angle X-ray scattering (T-ramp SAXS) from 255 to 335 K with molecular simulations to demonstrate that the hydration shells of the IgG-binding domain of Protein G (GB3) and the villin headpiece are remarkably temperature-sensitive. For proteins in the folded state, T-ramp SAXS data and explicit-solvent SAXS predictions consistently demonstrate decays of protein contrasts and radii of gyration with increasing temperature, which are shown to reflect predominantly temperature-sensitive, depleting hydration shells. The depletion is caused not merely by enhanced disorder within the hydration shells but also by partial displacements of surface-coordinated water molecules. Together, T-ramp SAXS and explicit-solvent SAXS calculations provide a novel structural view of the protein hydration shell, which underlies temperature-dependent processes such as cold denaturation, thermophoresis, or biomolecular phase separation.

electron density↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗
DOE OSTI2025

Molecular-Level Insights into the NMR Relaxivity of Gadobutrol Using Quantum and Classical Molecular Simulations

MRI is an indispensable diagnostic tool in modern medicine; however, understanding the molecular-level processes governing NMR relaxation of water in the presence of MRI contrast agents remains a challenge, hindering the molecular-guided development of more effective contrast agents. By using quantum-based polarizable force fields, the first-of-its-kind molecular dynamics (MD) simulations of Gadobutrol are reported where the 1 H NMR longitudinal relaxivity r 1 of the aqueous phase is determined without any adjustable parameters. The MD simulations of r 1 dispersion (i.e., frequency dependence) show good agreement with measurements at frequencies of interest in clinical MRI. Importantly, the simulations reveal key insights into the molecular level processes leading to r 1 dispersion by decomposing the NMR dipole–dipole autocorrelation function G(t) into a discrete set of molecular modes, analogous to the eigenmodes of a quantum harmonic oscillator. The molecular modes reveal important aspects of the underlying mechanisms governing r 1 , such as its multiexponential nature and the importance of the second eigenmodal decay. By simply analyzing the MD trajectories on a parameter-free approach, the Gadobutrol simulations show that the outer-shell water contributes ∼50% of the total relaxivity r 1 compared to the inner-shell water, in contrast to simulations of (nonchelated) gadolinium-aqua where the outer shell contributes only ∼15% of r 1 . The deviation between simulations and measurements of r 1 below clinical MRI frequencies is used to determine the low-frequency electron-spin relaxation time for Gadobutrol, in good agreement with independent studies.

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

Long-Range Dispersion Governs the Behavior of Near-Critical Fluids: Universal Scaling and Implications for Accurate Molecular Simulation

Computer simulations of near-critical and supercritical fluids often deviate from experimental results, a discrepancy commonly attributed to force-field resolutions and inaccuracies. We demonstrate that these errors primarily stem from using finite cutoff schemes for dispersion interactions, which become effectively long-range as the correlation length grows near the critical point. By employing the smooth particle-mesh Ewald method to account for full long-range dispersion, we show that coarse-grained models can also achieve high quantitative accuracy for n-undecane and propylbenzene. This approach enables the precise determination of density fluctuations and correlation lengths, which exhibit universal critical scaling and depend on the molecular size and shape. Our findings resolve a longstanding debate in molecular simulations of near-critical fluids, establishing that capturing long-wavelength fluctuations is essential for bridging the gap between the microscopic behavior and macroscopic critical phenomena.

Chen, Guang↗
DOE OSTI2025

Molecular Simulation of Functionalized Covalent Organic Framework Membranes for Inorganic Salt Separation

Covalent organic frameworks (COFs) enable molecular-level design of nanochannels for selective separation in pressure-driven membrane processes. Through variation of building blocks and, subsequently, the pore structure and chemistry, membrane performance can be tailored. This study employs nonequilibrium molecular dynamics simulations to theoretically demonstrate the tunable selectivity of COF membranes through a bottom-up functionalization approach. Water and salt transport are evaluated for six β-ketoenamine-linked COFs with varying multilayer thicknesses. For the thinnest multilayer (0.64–0.72 nm), all COFs exhibit low sodium sulfate (Na 2 SO 4 ) rejection (55–66%). However, 20 stacked sheets (6.4–7.2 nm) provide 67–98% rejection, with the sulfonated COF providing the highest Na 2 SO 4 rejection. Analysis of time-resolved ion density profiles reveals that solute rejection is primarily governed by interfacial exclusion arising from pore size and functional group chemistry. Although increasing salt rejection compromises water permeance, the permeance of all COF membranes is at least two orders of magnitude greater than that of a commercially available nanofiltration membrane. Overall, this work guides the rational design of COF membranes for aqueous salt separation.

Nanofiltration↗
DOE OSTI2026

Investigating Lignin Aggregation and Interactions with Solvents during γ-Valerolactone (GVL) Pretreatment: A Combined Small Angle Neutron Scattering and Molecular Simulations Study

The strong tendency of lignin to aggregate in solution, coupled with limited understanding of how its molecular structure governs this behavior, hinders its effective utilization in biorefineries. Here, in this study, we investigated the solution behavior of lignin extracted from poplar using γ-valerolactone/water (GVL/H2O, 9:1 wt/wt) through combined small-angle neutron scattering (SANS) and molecular dynamics (MD) simulations. Lignin samples obtained at 100 °C (L100) and 120 °C (L120) differed in β–O–4 content, hydroxyl distribution, and S/G ratio, enabling direct assessment of how molecular composition governs solvation and aggregation. SANS showed that L120 formed rigid and elongated cylindrical aggregates at 25 °C that transitioned to more flexible spheroidal structures by 50 °C and remained stable up to 80 °C, whereas L100 adopted globular aggregates that progressively collapsed with increasing temperature. MD simulations reinforced these observations by showing that S-rich (L120-like) oligomers had larger radii of gyration, stronger solvent coordination driven by methoxy groups, and fewer lignin–lignin contacts. In contrast, G-rich (L100-like) oligomers displayed persistent aggregation and lower solubility. Collectively, these results reveal that increased aromatic methoxylation enhances lignin–solvent interactions and suppresses self-association, whereas reduced methoxylation and higher β–O–4 content promote persistent aggregation into colloid-like structures with restricted solvent penetration into the aggregate interior.

biorefinery pretreatment↗
DOE OSTI2025

Relationship between microporous structure and light gas transport through glassy polymeric membranes revealed by molecular simulations

Microporous glassy polymers are attractive materials for gas separation membranes, due to their high permeability and tailorable selectivity, provided physical aging can be delayed. The archetypal microporous glassy polymer, PTMSP, can be blended with a hyper-crosslinked isatin–triptycene porous polymer network (PPN) to delay physical aging. However, while PPN is effective at reducing physical aging, it also affects the permeability of light gases through PTMSP. Molecular dynamics simulations were used here to shed fundamental light on the mechanisms responsible for these effects. Atomistic models are developed that satisfactorily reproduce experimental observations such as matrix density and cavity size distributions for neat PTMSP as well as for PTMSP–PPN blends. Analysis of the simulation results suggests that physical aging is delayed because the PPN inclusions slow down PTMSP relaxation while reducing the connectivity between free volume pockets. To understand how PPN inclusions affect light gas permeability, the atomistic models developed are used to probe CO 2 and CH 4 diffusion and sorption. These simulations are conducted for PTMSP matrices exhibiting varying density, towards reproducing experimental permeability data. Interrogating the simulation trajectories, it is discovered that while CH 4 travels preferentially through the free volume cavities, CO 2 preferentially interacts with the available surfaces, especially in the presence of PPN. These differences help interpret experimental observations. The transport of C 2 H 6 and H 2 S through PTMSP matrices was also investigated. These gases, and in particular H 2 S, were found to absorb within PTMSP, yielding very low diffusion coefficients. The ability to predict differences in diffusion pathways for various natural gas components unveils the possibility of engineering membranes to control permeability and selectivity towards large-scale applications.

Bao Le, Tran Thi [Univ. of Oklahoma, Norman, OK (U↗
NASA NTRSDate not supplied

Validation of Accurate Cryogenic Fluid Vapor-Liquid Boundary Conditions Via Molecular Simulations

Vapor-liquid interfaces drive many important phenomena in cryogenic fluid management, including heat transfer, evaporation, and capillary flow. Design of cryogenic fluid systems, such as propellant storage, requires accurate predictions of fluid behavior, including evaporation rates. Many models have been proposed for heat and mass transfer at vapor liquid interfaces, but the accuracy of these models in the context of cryogenic fluids has not been performed. We use molecular dynamics simulations, which allow for nanometer scale resolution of fluid phenomena, to evaluate the accuracy of a variety of vapor-liquid boundary conditions at evaporating and condensing interfaces. We find that an anisotropic temperature distribution is a critical ingredient for accurate prediction of intensive evaporation and condensation.

Daniel Vigil↗
DOE OSTI2025

Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning

Computational methods have demonstrated success in identifying virucidal agents, effectively contributing to the discovery of novel virucidal molecules. In this study, we developed a machine learning (ML) model, trained on a small dataset, to predict inhibitors of human enterovirus 71 (EV71), a pathological agent that causes severe disease in children and immunocompromised adults. Despite the dataset’s limitation, comprising of only 36 compounds tested, our ML framework demonstrated significant predictive capability. Notably, experimental validation revealed that five out of the eight compounds predicted by our model from the Chinese cosmetic material list exhibited virucidal activity. The inhibitor effects displayed by the main active compounds were further confirmed by molecular dynamics simulation. This underscores the potential of our AI-driven approach to bypass data constraints in identifying active molecules against viral pathogens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Unravelling the dynamics of the maturation protein in MS2 bacteriophage via molecular simulations

The MS2 bacteriophage capsid serves as a model system for studying viral structure and function. Mature MS2 virus consists of 178 capsid proteins and a single maturation protein (MP), which is essential for host receptor binding and infection initiation. Despite its critical role, the dynamic behavior of the capsid with the MP remains poorly understood. To address this, we conducted 0.5 µs all-atom molecular dynamics (MD) simulations of the MS2 capsid with and without the MP, revealing key insights into its structural dynamics. Our simulations showed that MP exhibits high flexibility, particularly in the “tip” and “side-loop” regions, which undergo significant motions that likely enhance its ability to engage with the F-pilus receptor. Detailed analysis of MP conformational states revealed that loop rearrangements around H357 enable transient switching between “semi-closed” and “open” conformations, suggesting a conformational selection mechanism for pilus binding. Additionally, ion interaction analyses revealed distinct sodium and chloride binding patterns, where sodium ions were mostly found at the outer capsid shell, while chloride ions interacted with the basic residues on the RNA-facing side. We also found that the presence of the MP enhances salt-bridge interactions, contributing to increased capsid stability, yet it does not significantly alter the pore sizes of pentameric and hexameric units. Together, these findings provide new insights into the functional role of the MP, highlighting its contribution to capsid stability and host receptor engagement. This study offers a foundation for understanding capsid dynamics relevant to viral infectivity and may guide future rational strategies aimed at disrupting host-virus interactions.

Capsid stability↗
DOE OSTI2025

Molecular simulation and artificial intelligence for the circular economy of bioenergy and bioproducts

The concept of the circular bioeconomy is a carbon neutral, sustainable system with zero waste. One vision for such an economy is based upon lignocellulosic biomass. This lignocellulosic circular bioeconomy requires CO 2 absorption from biomass growth and the efficient deconstruction of recalcitrant biomass into solubilized and fractionated biopolymers, which are then used as precursors for the sustainable production of high-quality liquid fuels, chemical bioproducts, and bio-based materials. Here, in this study, we summarize the roles that molecular dynamics (MD) simulations and machine learning (ML) are playing in overcoming several fundamental challenges hindering the adoption of a circular bioeconomy. Specifically, we discuss the role of MD and ML/AI in overcoming lignocellulose recalcitrance by designing biomass pretreatment methods to efficiently produce solubilized cellulose/lignin/hemicellulose and of that in improving energy-intensive manufacturing of biomass-based materials and their structural and mechanical properties. Quantum mechanical methods and MD simulations, in addition to offering a mechanistic understanding of biomass deconstruction and biomaterials design, can provide meaningful structural, energetics, and physiochemical properties as inputs to train AI/ML models. The ML models can guide the experimental prioritization of materials/solvents and process parameters that significantly accelerate the development of biofuel and biomaterial components of the circular bioeconomy.

Smith, Jeremy C. [Oak Ridge National Laboratory (O↗
DOE OSTI2026

Quantifying Membrane Structure and Dynamics during Bioproduct Production in Zymomonas mobilis by Molecular Simulation

The conversion of lignocellulosic biomass into biofuels and bioproducts by microbial biorefineries is central to a sustainable chemical industry. Zymomonas mobilis is one such biorefinery chassis and is resistant to ethanol stress, leading to its use in biomass conversion to biofuels and bioproducts. However, Z. mobilis growth is often inhibited by organic acids, aldehydes, alcohols, ketones, and amides found in biomass hydrolysate. The resulting slow growth inhibits production and as a result drives up the price for the resulting products. One hypothesis is that these molecules interact with or disrupt the bacterial membrane, triggering stress responses and hindering growth. To test this hypothesis at the molecular level, we employ all-atom molecular dynamics (MD) simulations to investigate lignocellulose-derived small molecules and their impact on a biologically relevant Z. mobilis membrane model. Simulations were conducted across a range of inhibitor concentrations from 0 to 2.5 mol %, analyzing key membrane properties such as area per lipid (APL), membrane thickness, lipid-order parameter (−S CH ), lateral diffusion coefficient (D xy ), and permeability coefficient (Pm). From simulation, we observed altered membrane structure and dynamics at these modest small molecule concentrations commonly found in hydrolysates. Generally, the membranes become thinner, with a higher area per lipid and lower-order parameter as the small molecule concentration increases. These trends are stronger for more hydrophobic molecules with greater hydrophobic bulk, as isobutanol, propanol, and propanoic acid showed greater membrane perturbations as the concentration increased compared to other small molecules. Tracking small molecule distributions directly in our equilibrium simulations allows us to determine concentration-dependent free energy profiles for these molecules. While the trends are noisy, generally the barriers to crossing the membrane decrease as the concentration increases, indicating that the membranes become leakier as small molecule concentrations rise. Comparing between native Z. mobilis membranes with hopanoids and membranes sharing the same phospholipid composition but without hopanoids, hopanoids stabilize and order the membrane for smaller molecules to maintain membrane structure but appear insufficient for larger hydrophobic molecules like isobutanol. These findings provide a mechanistic understanding of how small molecules found in biomass degradation streams interact with the Z. mobilis membrane, offering valuable insights for future strain engineering efforts to optimize biofuel and bioproduct synthesis from biomass feedstocks by highlighting limits to small molecule tolerance. This knowledge can guide the modification of membrane composition to develop more robust microbes, thereby improving microbial survival and yields in industrial contexts.

Singh, Nitin Kumar [Michigan State Univ., East Lan↗