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NASA NTRSDate not supplied

Interfacial thermodynamics of cryogenic fluids: The effect on non-condensable gas on fluid storage

Propellant tanks that contain cryogenic fluids (CFs) in low gravity conditions are usually pressurized with non-condensable (NC) gases for fast extraction. Unfortunately, the presence of NC gases causes CFs to exhibit higher boil-offs compared to pure CF systems. For optimal utilization of the cryogenic fuels, these higher boil-off rates must be minimized. Our goal is to quantify the effects NC gases have on the evaporation and condensation dynamics of CFs to develop strategies that mitigate the higher boil-offs. The hypothesis is that the NC gas accumulates around the CF’s liquid vapor interface in the Knudsen layer, creating a kinetic barrier for both evaporation and condensation. Testing this hypothesis with experimental methods is difficult due to the transient nature of the Knudsen layer, which is only a few nanometers thick. Furthermore, experimental capabilities are limited in low gravity conditions and extremely expensive. Accordingly, we approach this problem from a theoretical perspective. In this study, we employ molecular dynamics (MD) simulations to probe the interfacial mechanisms that affect CF’s evaporation and condensation at varying concentrations of NC gases. Specifically, we use nitrogen (N2) and oxygen (O2) as our CFs and neon (Ne) as our NC gas. Using MD simulations, we show that Ne accumulates at N2 liquid vapor interface across a wide range of Ne concentrations, thereby impeding mass transport of N2. Our simulations allow for direct computation of the molar flux as well as the mass accommodation coefficient (MAC) which can then be used as input parameters to continuum fluid dynamics (CFD) models for optimal storage tank design.

Michael Robert DeLyser↗

DOE OSTI2024

Atomic scale etching of diamond: insights from molecular dynamics simulations

Diamond is a promising material for multiple applications in quantum information processing and sensing as well as applications in microelectronics. However, diamond devices can be limited by surface defects that compromise charge stability and spin coherence, among others. Improved strategies in plasma etching of diamond could play an important role in minimizing or eliminating these defects. In this work, we explore plasma-assisted atomic scale etching of diamond using argon ions (Ar + ), hydrogen ions (H + ) and hydrogen atoms (H). We employ classical molecular dynamics (MD) simulations and test several interatomic potentials based on the Reactive Empirical Bond Order (REBO) form with comparisons to a variety of published experimental results. We performed MD simulations of low-energy hydrogen ($\leqslant$50 eV) and argon ( $\leqslant$200 eV) ion bombardment of diamond surfaces. Ar + bombardment can be used to locally smooth initially rough diamond surfaces via the formation of an amorphous C layer, the thickness of which increases with argon ion energy. Subsequent exposure with hydrogen ions (or fast neutrals) will selectively etch this amorphous C layer, leaving the underlying diamond layer mostly intact if the H energy is maintained below about 10 eV. The simulations suggest that combining Ar + smoothing with selective, near threshold energy H removal of amorphous C can be an effective strategy for diamond surface engineering, leading to more reliable and sensitive diamond color center devices.

74 ATOMIC AND MOLECULAR PHYSICS↗

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

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↗

NASA NTRS2007

Interactions of the C-terminal Domain of Human Ku70 with DNA Substrate: A Molecular Dynamics Study

NASA is developing a systems biology approach to improve the assessment of health risks associated with space radiation. The primary toxic and mutagenic lesion following radiation exposure is the DNA double strand break (DSB), thus a model incorporating proteins and pathways important in response and repair of this lesion is critical. One key protein heterodimer for systems models of radiation effects is the Ku(sub 70/80) complex. The Ku70/80 complex is important in the initial binding of DSB ends following DNA damage, and is a component of nonhomologous end joining repair, the primary pathway for DSB repair in mammalian cells. The C-terminal domain of Ku70 (Ku70c, residues 559-609), contains an helix-extended strand-helix motif and similar motifs have been found in other nucleic acid-binding proteins critical for DNA repair. However, the exact mechanism of damage recognition and substrate specificity for the Ku heterodimer remains unclear in part due to the absence of a high-resolution structure of the Ku70c/DNA complex. We performed a series of molecular dynamics (MD) simulations on a system with the subunit Ku70c and a 14 base pairs DNA duplex, whose starting structures are designed to be variable so as to mimic their different binding modes. By analyzing conformational changes and energetic properties of the complex during MD simulations, we found that interactions are preferred at DNA ends, and within the major groove, which is consistent with previous experimental investigations. In addition, the results indicate that cooperation of Ku70c with other subunits of Ku(sub 70/80) is necessary to explain the high affinity of binding as observed in experiments.

Hu, Shaowen↗

DOE OSTI2025

Modeling Equilibrium Solid–Liquid Interfaces under Effective Constant Chemical Potential Using Machine Learning Interatomic Potentials

The chemical potential (μ) of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot maintain constant chemical potential with reference to a targeted concentration or chemical potential under nonequilibrium or dynamic conditions, as solute species can migrate to the interface and deplete (or enrich) the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative quasi-constant chemical potential molecular dynamics (iqCμMD) simulation, which helps simulate targeted molar concentrations of species in solution. iqCμMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target bulk concentration (chemical potential), which allows simulation of the interface under the bulk conditions comparable to experiment. We demonstrate our approach using machine learning interatomic potential (MLIP)-based MD simulations of the Na 2 SO 4,aq –graphene interface, and to show the transferability of our approach, we also perform classical force field-based MD simulations of NaCl aq –air and NaCl aq –graphite interfaces, which produce comparable results to previous CμMD simulations. Our results also show that the iqCμMD approach efficiently achieves the desired bulk ion concentration within two iterations, and by utilizing MLIPs, we can achieve converged results using relatively small-scale simulations compared to previous CμMD simulations. By combining iqCμMD with MLIP-driven simulations, solid–liquid interfaces can be modeled under an effective constant chemical potential with DFT-level accuracy. Here, we show that iqCμMD offers a robust and simple computational framework for constant chemical potential simulations, as its only requirement is to be able to converge interfacial simulations with a measurable bulk region.

Chemical structure↗

DOE OSTI2025

Molecular Dynamics Simulations of Supercritical Carbon Dioxide and Water using TraPPE and SWM4-NDP Force Fields

The increased levels of carbon dioxide (CO 2 ) emissions due to the combustion of fossil fuels and the consequential impact on global climate change have made CO 2 capture, storage, and utilization a significant area of focus for current research. In most electrochemical CO 2 applications, water is used as a proton donor due to its high availability and mobility and use as a polar solvent. Additionally, supercritical CO 2 is a promising avenue for electrochemical applications due to its unique chemical and physical properties. Consequently, understanding the interactions between water and supercritical CO 2 is of great importance for future electrochemical applications. Molecular dynamics (MD) simulation is a powerful tool that enables atomistic-resolution dynamics of molecular systems, which can complement and guide future experimental investigations. This study employed atomistic MD to study the cosolubilities, codiffusivities, and structure of supercritical CO 2 and water systems, with a polarizable water model (SWM4-NDP) and a nonpolarizable CO 2 model (TraPPE). Additionally, ab initio MD simulations were used to better understand how atomistic polarizable/nonpolarizable models compare to explicit modeling of electron densities. The polarizable water model exhibited substantial improvement in water-associated properties. In conclusion, we anticipate the development of a compatible polarizable CO 2 model to yield similar improvement, providing a pathway for realizing novel high-pressure electrochemical systems.

25 ENERGY STORAGE↗

NASA NTRS2017

Small Ion Channel Linking Molecular Simulations and Electrophysiology

Ion channels are pore-forming protein assemblies that mediate the transport of small ions across cell membranes. Otherwise, membrane bilayers would be almost impermeable to ions incapable to traverse the low dielectric constant, hydrophobic membrane core. Ion channels are ubiquitous to all life forms. In humans and other higher organisms they play the central role in conducting nerve impulses, cardiac functions, muscle contraction and apoptosis. On the other extreme of biological complexity, viral ion channels (viroporins) influence many stages of the virus infection cycle either through regulating virus replication, such as entry, assembly and release or modulating the electrochemical balance in the subcellular compartments of host cells. Ion channels were crucial components of protocells. Their emergence facilitated adaptation of nascent life to different environmental conditions. The earliest ion channels must have been much simpler than most of their modern ancestors. Viral channels are among only a few naturally occurring models to study the structure, function and evolution of primordial channels. Experimental studies of these properties are difficult and often unreliable. In principle, computational methods, and molecular dynamics (MD) simulations in particular, can aid in providing information about both the structure and the function of ion channels. However, MD suffers from its own problems, such as inability to access sufficiently long time scales or limited accuracy of force fields. It is, therefore, essential to determine the reliability of MD simulations. We propose to do so on the basis of two criteria. One is channel stability on time scales that extend for several microseconds or longer. The other is the ability to reproduce the measured ionic conductance as a function of applied voltage. If both the stability and the calculated ionic conductance are satisfactory it will greatly increase our confidence that the structure and the function of a channel are described sufficiently accurately. To our knowledge, long time scale stability (approx.10 micro-sec) and the correct electrophysiology have been shown so far for only one channel - the synthetic LS3 hexamer). In this presentation, this approach will be discussed in application to two viral channels - Vpu, encoded by the HIV-1 genome and p7 of hepatitis C.

Pohorille, Andrzej↗

DOE OSTI2024

Structural basis of transcription: RNA polymerase II substrate binding and metal coordination using a free-electron laser

Catalysis and translocation of multisubunit DNA-directed RNA polymerases underlie all cellular mRNA synthesis. RNA polymerase II (Pol II) synthesizes eukaryotic pre-mRNAs from a DNA template strand buried in its active site. Structural details of catalysis at near-atomic resolution and precise arrangement of key active site components have been elusive. Here, we present the free-electron laser (FEL) structures of a matched ATP-bound Pol II and the hyperactive Rpb1 T834P bridge helix (BH) mutant at the highest resolution to date. The radiation-damage-free FEL structures reveal the full active site interaction network, including the trigger loop (TL) in the closed conformation, bonafide occupancy of both site A and B Mg 2+ , and, more importantly, a putative third (site C) Mg 2+ analogous to that described for some DNA polymerases but not observed previously for cellular RNA polymerases. Molecular dynamics (MD) simulations of the structures indicate that the third Mg 2+ is coordinated and stabilized at its observed position. TL residues provide half of the substrate binding pocket while multiple TL/BH interactions induce conformational changes that could allow translocation upon substrate hydrolysis. Consistent with TL/BH communication, a FEL structure and MD simulations of the T834P mutant reveal rearrangement of some active site interactions supporting potential plasticity in active site function and long-distance effects on both the width of the central channel and TL conformation, likely underlying its increased elongation rate at the expense of fidelity.

Science & Technology - Other Topics↗

DOE OSTI2025

Atomistic Investigation of Plastic Deformation and Dislocation Motion in Uranium Mononitride

Uranium mononitride (UN) is a promising advanced nuclear fuel due to its high thermal conductivity and high fissile density. However, many aspects of its mechanical behavior, particularly at reactor-relevant conditions, remain unclear. In this study, molecular dynamics (MD) simulations were employed to investigate the deformation behavior and dislocation motion in UN. We found that the Kocevski potential predicts the principal slip system as $\frac{1}{2}$ $\langle110\rangle${110}, aligning with experimental data. On the other hand, the Tseplyaev potential predicts slip to primarily occur on $\frac{1}{2}$ $\langle110\rangle${111}. MD simulations of stress–strain behavior were used to estimate the nanoindentation hardness, revealing that the Kocevski potential accurately predicts hardness even though it fails to model dynamic plasticity. Complete dislocation mobility functions have been fitted for the edge and screw dislocations in both the thermally activated and phonon-drag regimes. The 300 K linear mobility of the edge dislocation using the Tseplyaev potential was found to be 817 Pa -1 ·s -1 , whereas that of the screw dislocation using the Kocevski potential was found to be 4546 Pa -1 ·s -1 . At intermediate stresses, we observed that the subsonic steady-state motion of the edge dislocation in UN is intermittently interrupted by velocity jumps, reaching the average sound velocity. Finally, the threshold Schmid stress is calculated as 179–197 MPa, which gives an upper-limit estimate of the uniaxial yield stress of polycrystalline UN of 548–603 MPa. These findings, including the fitted dislocation mobility function, provide essential input for future plasticity and dislocation dynamics models of nuclear fuels.

36 MATERIALS SCIENCE↗

NASA NTRS2018

Exploring Li-Air Batteries for High Specific Energy and High Power Applications: A Simulation Study

Commercialization of lithium-air batteries face many challenges, such as electrolyte decomposition, short cycle life, low energy efficiency, low power density, etc. However, commercialization of Li-air batteries for mass sensitive applications such as electric vehicles, portable power source, and drones is more challenging due to additional constraints of safety, electrolyte evaporation, high specific energy requirements, and reliable discharge times. In this presentation, we will present our finite element simulation results comparing Li-O2 and Li-air batteries using power density, energy density, specific power, and discharge times as metrics to evaluate different electrolytes and electrode geometry to reduce total mass and maximize discharge current. We use a finite element model and a discharge product model developed in which is based on porous electrode and concentrated electrolyte theories and the discharge product is modeled using quantum tunneling; for reaction kinetics and oxygen diffusion, an improved model was used. The electrolyte properties such as ion conductivity and ion diffusion were obtained from Molecular Dynamics (MD) simulations while the other parameters for the finite element model were calibrated to match experiments at high discharge current densities (>1.5 mA/cm2). The mass densities of different electrolytes were computed using MD simulations as well. For this presentation, we examine the practical electrochemical mass of a system at different current ratings, the sensitivity of mass to the use of ambient air as compared to pure oxygen as well as the electrolyte, which affects maximum current density and the total mass associated with the electrolyte (which includes the mass of additional components), and, the optimization of battery geometry for total discharge time, average discharge voltage, maximum discharge current, and minimum electrochemical mass.

Mehta, M.↗

DOE OSTI2025

Synthesis challenges, thermodynamic stability, and growth kinetics of La–Si–P ternary compounds

Although many new compounds have been recently predicted with the help of machine learning, the successful experimental synthesis of these compounds remains challenging. Computational insights about the thermodynamic stability and phase formation kinetics among the ground state and competing metastable phases are highly desirable to rationalize and attempt to overcome synthesis challenges experimentally. In this work, we explore synthetic challenges within ternary La–Si–P compounds through feedback between experimental and computational studies. We discuss the experimental challenges in forming three computationally predicted ternary phases (La 2 SiP, La 5 SiP 3 , and La 2 SiP 3 ). To understand the synthetic challenges, we performed molecular dynamics (MD) simulations using an accurate and efficient artificial neural network machine learning (ANN-ML) interatomic potential. We study the phase stability and formation kinetics of these ternary phases in relation to the reported and synthesized La 2 SiP 4 phase. While the growth of the La 2 SiP 4 phase can be reproduced by our MD simulation, our results indicate that the rapid formation of a Si-substituted LaP crystalline phase is a major barrier to the synthesis of the predicted La 2 SiP, La 5 SiP 3 , and La 2 SiP 3 ternary compounds, agreeing well with experimental observations. Our simulations also suggest that there is a narrow temperature window in which the La 2 SiP 3 phase can be grown from the solid–liquid interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗

DOE OSTI2025

Enhanced Ion Transport and Molecular Packing Stability in Asymmetric 2D Nanostructured π‐Conjugated Thieno[3,2‐b]Thiophene‐Based Liquid Crystal

Organic semiconductors based on liquid crystal (LC) molecules have attracted increasing interest. Here, in this work, two linear LCs based on 2,5‐bis(thien‐2‐yl)thieno[3,2‐b]thiophene (BTTT) mesogen are designed and synthesized, including BTTT/dEO3 with two symmetrically attached tri(ethylene oxide) groups and BTTT/mEO6 with one asymmetrically attached hexa(ethylene oxide) group. These two molecules have comparable functional‐group compositions but different molecular geometries, leading to their moderately different material performances. Both LCs show smectic mesophases with relatively low transition temperatures as confirmed by differential scanning calorimetry and polarized optical microscopy. A combination of experimental grazing incidence wide‐angle X‐ray scattering and molecular dynamics (MD) simulations reveals a herringbone packing motif of BTTT segments in both LCs while a smaller molecular tilt angle in BTTT/mEO6. Ionic conductivities are measured by doping LCs with different amounts of ionic dopants, lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). BTTT/mEO6 shows better smectic phase stability to higher LiTFSI doping ratios. Both LCs exhibit similar ionic conductivities in the smectic phases, but BTTT/mEO6 outperforms BTTT/dEO3 by a factor of three in the amorphous phase at higher temperatures. MD simulations, performed to examine the ion solvation environment, reveal that BTTT/mEO6 is more efficient in coordinating Li‐ions and screening their interactions with TFSI‐ions which further promote ionic transport.

grazing incidence wide-angle X-ray scattering↗

DOE OSTI2024

Rational Design of High-Performing Electrodes in Energy Storage Devices

Our approach in this project is to control conductivities of different ions by modulating the pore sizes in MOFs as sulfur hosts in cathodes. To validate our hypothesis that controlling the pore size of MOFs can effectively prevent LiPSs shuttling while allowing sufficient Li ion transport, which will ensure electrochemical cycling performance, we performed molecular dynamics (MD) simulations using a series of UiO MOFs with different linker sizes including LiPSs and electrolyte components (1M LiTFSI in DME/DOL 1:1 volume) in pores. Specifically, UiO-66, UiO-67, and UiO-68 are Zr-based MOFs with a linker length of 5.67 Å, 10.07 Å, and 14.35 Å, respectively, leading to different pore diameters of 8.4 Å, 12.8 Å, and 17.8 Å. We calculated the mean-squared displacements of Li and LiPS from MD simulations at 300 K for 50 ns or over to assess their mobility in MOFs.

25 ENERGY STORAGE↗

DOE OSTI2024

A transient site balance model for atomic layer etching

We present a transient site balance model of plasma-assisted atomic layer etching of silicon (Si) with alternating exposure to chlorine gas (Cl 2 ) and argon ions (Ar + ). Molecular dynamics (MD) simulation results are used to provide parameters for the model. The model couples the dynamics of a top monolayer surface region ('top layer') and a perfectly mixed subsurface region ('mixed layer'). The differential equations describing the rates of change of the Cl coverage in the two layers are transient mass balances. Model predictions include Cl coverages and rates of etching of various species from the surface as a function of Cl 2 or Ar + fluence. The simplified phenomenological model reproduces the MD simulation results well over a range of conditions. Comparing model predictions directly to experimental optical emission spectroscopy data, as reported in a previous paper, provides further evidence of the accuracy of the model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRSDate not supplied

Influence of Microstructural Features on Austenite-Martensite Interfaces in NiTi Shape Memory Alloys

Shape memory alloys (SMAs) exhibit several unique thermomechanical properties due to a reversible martensitic phase transformation. A key aspect of this transformation is the austenite-martensite interface. Here we present a molecular dynamics (MD) simulation methodology to study the atomic-scale features of austenite-martensite interface migration under near-equilibrium conditions. In single crystals, the interfaces migrate rapidly with only a small thermodynamic driving force. In polycrystals, however, interface migration is significantly impeded due to the change in orientation relationship at grain boundaries and the stored elastic energy resulting from microstructural constraints. This behavior can be linked to several mechanisms associated with transformation width and hysteresis in SMAs, properties of great importance for applications involving actuation. Additionally, we will present preliminary MD simulation results on the influence of precipitates on the formation and migration of austenite-martensite interfaces.

Gabriel Plummer↗

NASA NTRSDate not supplied

Influence of Microstructural Features on Austenite-Martensite Interfaces in NiTi Shape Memory Alloys

Gabriel Plummer↗