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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2020

Effects of sodium and calcium chloride ionic stresses on model yeast membranes revealed by molecular dynamics simulation

As efforts to move a renewable economy grow, it will be necessary to make use of microbial conversion strategies for the production of novel materials or the upgrading of waste to high-value products. One critical technical challenge currently limiting waste upgrading remains the difficulty in obtaining single-pot conversion techniques where physical, chemical, and biological conversion are performed in a single step. To overcome this challenge, a detailed understanding of how different stresses impact microbial membrane stability will be necessary. In this work, using all-atom molecular dynamics simulations, we examine the impacts of moderate concentrations of NaCl and CaCl 2 on a model yeast plasma membrane. Weak, though statistically significant, changes in membrane morphology and dynamics functions are observed that are consistent with swelling and stiffening. Additionally, an examination of the ion-lipid contacts and the behavior of water at the water-membrane interface suggests that the impacts of these common salts may, in part, be mediated through changes to water-membrane hydrogen-bonding and hydration water dynamics.

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

High-temperature chromium diffusion in austenitic stainless steel: Ab initio molecular dynamics simulations

Chromium self-diffusion through stainless steel (SS) matrix and along grain boundaries is an important mechanism controlling SS structural materials corrosion. For this work, Cr diffusion in austenitic SS was simulated using canonical ab initio molecular dynamics with realistic models of type-316 SS bulk, with and without Cr vacancies, and a low-energy Σ3 twin boundary typically observed at active corrosion sites. Cr self-diffusion coefficients at 750 and 850 °C calculated using Einstein’s diffusion equation are 4.2 × 10 –6 and 8.1 × 10 –6 Å 2 ps –1 in pristine bulk, 3.8 × 10 –3 and 5.5 × 10 –3 Å 2 ps –1 in bulk including Cr vacancies, and 9.5 × 10 –2 and 1.0 × 10 –1 Å 2 ps –1 at a Σ3[1 1 1]60° twin boundary.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The mechanical response of micron-sized molecular crystals

Abstract Microstructures and corresponding properties of compacted powders ultimately depend on the mechanical response of individual particles. In principle, computational simulations can predict the results of powder compaction processes, but the selection of appropriate models for both particle–particle interactions and particle deformations across all relevant length scales remain nontrivial tasks, especially in material systems lacking detailed mechanical property information. The work presented here addresses these issues by conducting uniaxial compressions in situ inside of a scanning electron microscope to characterize the mechanical response of individual micron-sized particles of a molecular crystal, hexanitrohexaazaisowurtzitane (CL-20). This experimental approach enabled the collection of quantitative force and displacement data alongside simultaneous imaging to capture morphology changes. The results reveal information about elastic deformation, yield, plastic deformation, creep, and fracture phenomena. Accordingly, this work demonstrates a generalizable approach for assessing the mechanical response of individual micron-sized molecular crystal particles and utilizing those responses in particle-level models. Graphic abstract

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantum Sieving for Isotopic Separations of Gases Using Porous Materials─30 Years of Progress

Here, this paper reviews theoretical and experimental efforts to establish microporous materials that can be used to separate isotopologues of molecular gases such as H 2 and D 2 . Emphasis is placed on use of simplified models to highlight the quantum phenomena that make these separations possible. In equilibrium adsorption, differences in zero point energy in the adsorbed states of molecules favor binding of heaver isotopologues (e.g. D 2 relative to H 2 ). Experimental data showing this effect was reported as early as 1933, but the theoretical work of Beenakkers et al. in 1995 [Chem. Phys. Lett. 232 (1995) 379-382] spurred modern efforts to develop materials with strong so-called quantum sieving. In porous materials where the transition state for molecular diffusion is more strongly confined than the energy minima associated with equilibrium adsorption, differences in zero point energies between these two sites can lead to isotopic differences in molecular diffusivities. This effect favors diffusion of heavier species (e.g. D 2 ) relative to lighter species (e.g. H 2 ). This so-called kinetic quantum sieving has been observed experimentally in porous carbons and in porous organic cage materials. We show that quantum tunneling, which favors hopping of lighter species across energy barriers, diminishes the strength of kinetic quantum sieving but that it appears to make only a small contribution to the net molecular diffusivities in many porous materials of practical interest.

Sholl, David S. [Oak Ridge National Laboratory (OR↗
DOE OSTI2021

Temperature-dependent model of helium bubble expansion and bursting in tungsten

Continuum-scale models that can reliably predict the behavior of helium in tungsten are of interest to the fusion community due to the projected impact of these materials on fusion reactor operation. In this work, we perform molecular dynamics simulations of spherical helium bubbles of various sizes in tungsten at different temperatures and depths with the goal of determining a mathematical model of the pressure and density at which the bubbles initially expand or burst as a function of depth, size, temperature, and surface orientation. The bubbles are small enough that their loop-punching pressures cannot be accurately predicted with continuum mechanics, and their expansion behavior is important, as it appears to cause many of the features observed on helium-irradiated tungsten surfaces. We vary the temperature, bubble size, bubble depth, and surface orientation in each case, recording the bubble pressure and density that result in bubble expansion. An exponential function with three adjustable parameters is found to fit the results well; the parameters that best fit our results are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Temperature-based reactive flow model for TATB plastic bonded explosives

A new reactive flow model is presented for TATB-based plastic bonded explosives, applicable to shock initiation and detonation problems of differing initial temperature. Temperature disequilibrium is assumed between unreacted explosive, material in the vicinity of compressed defects (called hot spots), and detonation products. The model incorporates temperature-dependent decomposition reaction rates. Arrhenius model parameters were derived from subscale quantum-based molecular dynamics simulations of shock-induced TATB decomposition. Further, a model of detonation carbon aggregation is incorporated, describing the late-time slow release of energy inherent to detonation in TATB-based materials. The predictive ability of the model in the shock initiation regime is tested against recent thin pulse experiments on PBX 9502. Here, the model is found to perform equally well in predicting the size effect curve of ambient, cold, and hot PBX 9502 rate sticks.

Chemistry - Chemical explosives↗
DOE OSTI2022

Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

Ab initio molecular dynamics (AIMD) has become one of the most popular and robust approaches for modeling complicated chemical, liquid, and material systems. However, the formidable computational cost often limits its widespread application in simulations of the largest-scale systems. Furthermore, the situation becomes even more severe in cases where the hydrogen nuclei may be better described as quantized particles using a path integral representation. Here, we present a computational approach that combines machine learning with recent advances in path integral contraction schemes, and we achieve a 2 orders of magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.

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

Transferring a Molecular Foundation Model for Polymer Property Predictions

Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and material discovery. Self-supervised pretraining of transformer models requires large-scale data sets, which are often sparsely populated in topical areas such as polymer science. Further, state-of-the-art approaches for polymers conduct data augmentation to generate additional samples but unavoidably incur extra computational costs. In contrast, large-scale open-source data sets are available for small molecules and provide a potential solution to data scarcity through transfer learning. In this work, we show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieves comparable accuracy to those trained on augmented polymer data sets for a series of benchmark prediction tasks.

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

Unlocking High Capacity and Reversible Alkaline Iron Redox Using Silicate-Sodium Hydroxide Hybrid Electrolytes

Alkaline iron (Fe) batteries are attractive due to the high abundance, low cost, and multiple valent states of Fe but show limited columbic efficiency and storage capacity when forming electrochemically inert Fe 3 O 4 on discharging and parasitic H 2 on charging. Herein, sodium silicate is found to promote Fe(OH) 2 /FeOOH against Fe(OH) 2 /Fe 3 O 4 conversions. Electrochemical experiments, operando X-ray characterization, and atomistic simulations reveal that improved Fe(OH) 2 /FeOOH conversion originates from (i) strong interaction between sodium silicate and iron oxide and (ii) silicate-induced strengthening of hydrogen-bond networks in electrolytes that inhibits water transport. Furthermore, the silicate additive suppresses hydrogen evolution by impairing energetics of water dissociation and hydroxyl de-sorption on iron surfaces. In conclusion, this new silicate-assisted redox chemistry mitigates H 2 and Fe 3 O 4 formation, improving storage capacity (199 mAh g -1 in half-cells) and coulombic efficiency (94 % after 400 full-cell cycles), paving a path to realizing green battery systems built from earth-abundant materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Reversing inverse Hall-Petch and direct computation of Hall-Petch coefficients

A 2D bicrystal atomistic model of dislocation transmission through a Σ11<101>{131} symmetric-tilt grain boundary reveals details of Hall-Petch breakdown in the single dislocation regime in Al, Ni, and Cu. Partly based on a previous study, this research determines the stress required for a single dislocation to be transmitted through the Σ11 boundary and finds that single dislocation transmission typically deviates from Hall-Petch behavior because the leading partial dislocation becomes trapped in the boundary and emits glissile grain boundary disconnections that cause boundary sliding deformation at stresses well below boundary transmission stresses. Thus, mechanisms of inverse Hall-Petch, namely grain boundary shear and sliding, are shown to operate in lieu of grain boundary transmission in the single dislocation regime. However, by controlling the applied stresses, inverse Hall-Petch can be reversed and typical Hall- Petch grain boundary transmission regained. This, in turn, allows the direct computation of Hall-Petch coefficients. Further, a second dislocation on the same slip plane preserves typical Hall-Petch behavior for Al and Ni and does not lead to boundary sliding events, thus implying a critical grain size for Hall-Petch breakdown based on the pileup model.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Bead–Spring Simulation of Ionomer Melts—Studying the Effects of Chain-Length and Associating Group Fraction on Equilibrium Structure and Extensional Flow Behavior

Ionomers are associative polymers with diverse applications ranging from selective membranes and high-performance adhesives to abrasion- and chemical-resistant coatings, insulation layers, vacuum packaging, and foamed sheets. Within equilibrium melt, the ionic or associating groups are known to form thermally reversible, associative clusters whose presence can significantly affect the system’s mechanical, viscoelastic, and transport properties. It is, thus, of great interest to understand how to control such clusters’ size distribution, shape, and stability through the designed choice of polymer architecture and the ionic groups’ fraction, arrangement, and interaction strength. In this work, we represent linear associating polymers using a Kremer–Grest type bead–spring model and perform large-scale MD simulations to explore the effect of polymer chain-length (l) and fraction (f s ) of randomly placed associating groups on the size distribution and stability of formed clusters. We consider different chain-lengths (below and above entanglement), varying fractions of associating groups (represented by ‘sticky’ beads) between 5 and 20%, and a fixed sticky–sticky nonbond interaction strength of four times that between regular non-associating beads. For all melts containing associating groups the equilibrium structure factor S(q) displays a signature ionomer peak at low wave vector q whose intensity increases with increasing f s and l. The average cluster size Nc increases with f s . However, the effect of chain-length on Nc appears to be pronounced only at higher values of f s . Under extensional flows, the computed stress (and viscosity) is higher at higher f s and l regardless of strain rate. Beyond a critical strain rate, we observe fragmentation of the associative clusters, which has interesting effects on the stress/viscous response.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Microstructure, Transport, and Mechanics of Compacted Clay Simulated at the 0.1 μm Scale (1400 Smectite Clay Particles) Using a Coarse-Grained Model with Explicit Counterions

Clay-rich geomaterials play a critical role in many subsurface systems. The macroscale properties of these materials (low permeability, high ionic conductivity, high swelling pressure, etc.) are sensitive to molecular-level adsorption and hydration interactions at clay−water interfaces. Efforts to develop multiscale simulation approaches to predict these properties reveal a scale gap between atomistic simulations (typically limited to systems smaller than 10 nm) and continuum-scale models (which use computational grid elements with dimensions ≳ 10 μm). In this study, we present a coarse-grained (CG) framework that partly bridges this gap by simulating compacted smectite clay assemblages with dimensions of 0.1 μm containing 1,400 clay particles across a range of dry densities (1,050 to 1,850 kg·m −3 ) and Na/Ca counterion compositions (Na fraction ranging from 0.2 to 1). The simulated systems, along with their reconstructed binary three-dimensional pore networks, are used to evaluate the microstructure, pore size distribution, tortuosity, ion diffusivity, and swelling pressure of compacted smectite clay. Results show that our approach captures important features of the mesoscale heterogeneity of compacted clays, including tactoid formation, hierarchical porosity, and anisotropic pore networks. Results also reveal how compaction and counterion composition govern emergent behaviors, including dominant pore sizes, directional transport, and electrochemical response. This work highlights the potential of CG simulations to bridge molecular and continuum scales and to advance geotechnical and environmental applications involving clay-rich materials as well as related nanoporous media such as geopolymers and calcium-silicate-hydrate. However, the results also suggest that accurate prediction of certain microstructural and mechanical properties (e.g., swelling pressure) may require even larger-scale systems on the order of 1 μm.

02 PETROLEUM↗
DOE OSTI2021

Dislocation transmission across Σ3{112} incoherent twin boundary: a combined atomistic and phase-field study

Grain boundaries (GBs) in polycrystalline materials act as impediments to dislocation motion and result in strengthening. Understanding slip transmission through GBs, specifically twin boundaries, is essential to understand the plastic deformation behavior of polycrystalline fcc materials. Here the interaction between a glide dislocation and Σ3{112} incoherent twin boundary (ITB) in copper is investigated using a combined atomistic and mesoscale approach. The material parameters and structure of the GB in the mesoscale phase field dislocation dynamics (PFDD) model are informed from Molecular Statics (MS) simulations. The structural unit of the ITB consists of an array of three partial dislocations. The interaction between a glide dislocation impinging on each of the GB partial dislocations is investigated using both PFDD and Molecular Dynamics (MD) with two boundary conditions. Transmission planes predicted by both PFDD and MD (NVT) are in agreement, and show that not all transmission events are direct. Critical transmission stresses predicted by PFDD are in the range of 276 MPa to 1380 MPa, while MD predictions are in the range from 100 MPa to 700 MPa. The PFDD and MD predictions of slip transmission are explained using dislocation theory based on isotropic linear elasticity.

36 MATERIALS SCIENCE↗
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 OSTI2021

Three-state harmonic models for photoinduced charge transfer

A widely used strategy for simulating the charge transfer between donor and acceptor electronic states in an all-atom anharmonic condensedphase system is based on invoking linear response theory to describe the system in terms of an effective spin-boson model Hamiltonian. Extending this strategy to photoinduced charge transfer processes requires also taking into consideration the ground electronic state in addition to the excited donor and acceptor electronic states. In this paper, we revisit the problem of describing such nonequilibrium processes in terms of an effective three-state harmonic model. We do so within the framework of nonequilibrium Fermi’s golden rule (NE-FGR) in the context of photoinduced charge transfer in the carotenoid–porphyrin–C 60 (CPC 60 ) molecular triad dissolved in explicit tetrahydrofuran (THF). To this end, we consider different ways for obtaining a three-state harmonic model from the equilibrium autocorrelation functions of the donor–acceptor, donor–ground, and acceptor–ground energy gaps, as obtained from all-atom molecular dynamics simulations of the CPC 60 /THF system. The quantum-mechanically exact time-dependent NE-FGR rate coefficients for two different charge transfer processes in two different triad conformations are then calculated using the effective three-state model Hamiltonians as well as a hierarchy of more approximate expressions that lead to the instantaneous Marcus theory limit. Our results show that the photoinduced charge transfer in CPC 60 /THF can be described accurately by the effective harmonic three-state models and that nuclear quantum effects are small in this system.

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

Associative polymers with controlled sticker placement: How reversible bond distribution and density govern polymer dynamics

Associative polymers with precisely arranged stickers offer opportunities to program material properties with molecular precision. Yet, it remains unclear how the placement and fraction of stickers dictate structure, dynamics, and macroscopic properties. By developing a model unentangled polymer system with hydrogen-bonding stickers, we show that randomly distributed stickers neither form clusters nor change flow properties, whereas stickers placed at chain ends drive nanocluster formation even at low concentrations. Adding more end stickers produces a rubbery plateau spanning eight decades in frequency with two distinct relaxation timescales, in contrast to the single plateau predicted by the classic sticky Rouse model. These results demonstrate that sticker distribution dictates whether associative polymers undergo nanocluster formation or microphase separation, while substantial alterations in dynamics and viscoelasticity require both sticker aggregation and thermomechanical stability of associated domains. Our findings resolve a longstanding debate on associative polymer dynamics and provide molecular design rules for programmable soft materials.

36 MATERIALS SCIENCE↗