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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Effect of Entropic Constraints on the Thermodynamics of Molecular Adsorption in Nano‐Porous Materials

Abstract Gas separation is a critical industrial process that consumes a significant amount of energy due to the widely used techniques that are currently employed. Adsorptive materials—such as metal–organic frameworks (MOFs)—show promise as an energy‐efficient alternative. Of particular current interest are novel, temperature‐dependent separation processes in MOFs, such as the recently reported separation of ternary isomeric hydrocarbon mixtures within one and the same material. However, the mechanisms of these highly desirable separations remain poorly understood. Herein, through a combination of ab initio simulations and statistical mechanics, it is shown that the temperature dependence is the result of a constraint on the guest molecule's entropic degrees of freedom when loaded into the MOF, caused by the fortuitous tight fitting of the guest inside the pore. While the framework applies to all molecular adsorption in porous media, it is essential for the description of large molecules in small pores, which is demonstrated here using the separation of C6 isomers in Ca(H 2 tcpb) as a test case. The developed framework and analysis not only reveal the reason why separation occurs but also predict the temperatures at which it takes place, thus opening the door to newly designed MOFs with tailor‐made precision.

Chemistry↗
DOE OSTI2024

Experimental and Computational Study of Pyrogenic Carbonaceous Matter Facilitated Hydrolysis of 2,4-Dinitroanisole (DNAN)

This study investigated the reaction pathway of 2,4-dinitroanisole (DNAN) on the pyrogenic carbonaceous matter (PCM) to assess the scope and mechanism of PCM-facilitated surface hydrolysis. DNAN degradation was observed at pH 11.5 and 25 °C with a model PCM, graphite, whereas no significant decay occurred without graphite. Experiments were performed at pH 11.5 due to the lack of DNAN decay at pH below 11.0, which was consistent with previous studies. Graphite exhibited a 1.78-fold enhancement toward DNAN decay at 65 °C and pH 11.5 relative to homogeneous solution by lowering the activation energy for DNAN hydrolysis by 54.3 ± 3.9%. This is supported by our results from the computational modeling using Car–Parrinello simulations by ab initio molecular dynamics/molecular mechanics (AIMD/MM) and DFT free energy simulations, which suggest that PCM effectively lowered the reaction barriers by approximately 8 kcal mol –1 compared to a homogeneous solution. Quaternary ammonium (QA)-modified activated carbon performed the best among several PCMs by reducing DNAN half-life from 185 to 2.5 days at pH 11.5 and 25 °C while maintaining its reactivity over 10 consecutive additions of DNAN. We propose that PCM can affect the thermodynamics and kinetics of hydrolysis reactions by confining the reaction species near PCM surfaces, thus making them less accessible to solvent molecules and creating an environment with a weaker dielectric constant that favors nucleophilic substitution reactions. Nitrite formation during DNAN decay confirmed a denitration pathway, whereas demethylation, the preferred pathway in homogeneous solution, produces 2,4-dinitrophenol (DNP). Denitration catalyzed by PCM is advantageous to demethylation because nitrite is less toxic than DNAN and DNP. These findings provide critical insights for reactive adsorbent design that has broad implications for catalyst design and pollutant abatement.

2,4-dinitroanisole (DNAN)↗
DOE OSTI2025

Methanol at Water–TiO 2 Interfaces: Free Energies of Water and Methanol Dissociation

Methanol adsorption on TiO 2 surfaces has long been studied due to its role in enhancing photocatalytic hydrogen evolution, yet how it modulates surface chemistry under aqueous conditions remains little understood. Using molecular dynamics with an ab initio-based deep neural network potential, we find that methanol adsorption induces markedly different effects on the aqueous surfaces of anatase and rutile, the two common phases of TiO 2 . In anatase, methanol adsorption significantly enhances water dissociation, which is otherwise rare at the neat water interface. This enhancement arises from an alternative dissociation pathway mediated by surface-bound methoxyl groups. In contrast, methanol adsorption tends to suppress water dissociation on rutile, replacing it with thermodynamically favored methanol dissociation. Overall, methanol adsorption in an aqueous environment alters not only the availability of key reactive intermediates involved in hydrogen evolution but also the hydrogen source, which turns out to be primarily methanol on rutile, whereas both water and methanol are consumed on anatase. These results provide mechanistic insights into the coupled roles of organic adsorbates and water at photocatalytic interfaces, with implications on how methanol enhances the activity of H 2 evolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Identification of high-dielectric constant compounds from statistical design

Abstract The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 < ϵ < 101) and large band gaps (2.9 < E g (eV) < 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu 5 SiCl 6 O 4 and HoClO) and are shown to be thermodynamically stable against common semiconductors via phase diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20 < ϵ < 40) and band gaps (2.3 < E g (eV) < 2.7). While the ANN training-data are obtained from the Materials Project, the search-space consists of materials from the Open Quantum Materials Database (OQMD)—demonstrating a successful implementation of cross-database materials design. Overall, we report the dielectric properties of 17 materials calculated using ab initio calculations, that were selected in our design workflow. The dielectric materials with high-dielectric properties predicted in this work open up further experimental research opportunities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computing Reaction Kinetics with MC-PDFT–OPESf: Combining Multireference Electronic Structure Theory and Enhanced Sampling

Accurate rate constants are crucial for understanding and optimizing catalytic reactions mediated by enzymes, metalloproteins, and heterogeneous catalysts. These systems frequently present a dual computational challenge. Multiconfigurational reaction sites require multireference techniques for the accurate treatment of the electronic structure, and high activation barriers prevent efficient sampling of unbiased reactive transitions. In this work, we combine multiconfiguration pair-density functional theory (MC-PDFT) as an accurate and efficient multireference electronic structure method with on-the-fly probability-enhanced sampling flooding (OPESf) as an enhanced sampling method capable of accelerating reactive transitions. We demonstrate the approach on the Diels–Alder [4+2] cycloaddition between cis-butadiene and ethene as a reaction characterized by a large activation barrier and multireference character. MC-PDFT–OPESf provides reaction rates in agreement with experiments at a fraction of the computational cost required by conventional unbiased ab initio calculations. Here, we propose MC-PDFT–OPESf as an efficient approach for computing kinetics in strongly correlated molecular systems.

Chemical calculations↗
DOE OSTI2024

Additively manufactured Ni-20Cr to V functionally graded material: Computational predictions and experimental verification of phase formations

Throughout this work, a database for the Cr-Ni-V system was constructed by modeling the binary Cr-V and ternary Cr-Ni-V systems using the CALPHAD approach aided by density functional theory (DFT)-based first-principles calculations and ab initio molecular dynamics (AIMD) simulations. To validate this new database, a functionally graded material (FGM) using Ni-20Cr and elemental V was fabricated using directed energy deposition additive manufacturing (DED AM) and experimentally characterized. The deposited Ni-20Cr was pure fcc phase, while increasing the amount of V across the gradient resulted in the formation of sigma phase, followed by the bcc phase. The experimentally measured phase data was compared with computational predictions made using a Cr-Ni-V thermodynamic database from the literature as well as the database developed in the present work. The newly developed database was shown to better predict the experimentally observed phases due to its accurate modeling of binary systems within the database and the ternary liquid phase, which is critical for accurate Scheil calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Conformationally Adaptable Extractant Flexes Strong Lanthanide Reverse-Size Selectivity

Chemical selectivity is traditionally understood in the context of rigid molecular scaffolds with precisely defined local coordination and chemical environments that ultimately facilitate a given transformation of interest. By contrast, nature leverages dynamic structures and strong coupling to enable specific interactions with target species in otherwise complex media. Taking inspiration from nature, we demonstrate unconventional selectivity in the solvent extraction of light over heavy lanthanides using a conformationally flexible ligand called octadecyl acyclopa (ODA). This novel ligand forms pseudocyclic molecular complexes with lanthanide ions at organic/aqueous interfaces, revealed by vibrational sum frequency generation spectroscopy. These complexes are extracted into the organic phase, where femtosecond structural dynamics are probed by two-dimensional infrared spectroscopy and ab initio molecular dynamics simulations to mechanistically frame the macroscopic selectivity trends. We find larger-than-expected structural fluctuations and bond lengths for heavy Ln–ODA complexes that arise from an inability of ODA to contort around the smaller ions to satisfy all would-be bonding interactions, despite forming some individually strong bonds. This finding contrasts with the binding of ODA with lighter lanthanides where, despite individually weaker bonds, collective interactions manifest that minimize structural fluctuations and give rise to enhanced thermodynamic stability. Furthermore, these results point to a new paradigm where conformational dynamics and cumulative bonding interactions can be used to facilitate unconventional chemical transformations.

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

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗
DOE OSTI2024

Exploring Cr and molten salt interfacial interactions for molten salt applications

Molten salts play an important role in various energy-related applications such as high-temperature heat transfer fluids and reaction media. However, the extreme molten salt environment causes the degradation of materials, raising safety and sustainability challenges. A fundamental understanding of material–molten salt interfacial evolution is needed. This work studies the transformation of metallic Cr in molten 50/50 mol% KCl–MgCl 2 via multi-modal in situ synchrotron X-ray nano-tomography, diffraction and spectroscopy combined with density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. Notably, in addition to the dissolution of Cr in the molten salt to form porous structures, a δ-A15 Cr phase was found to gradually form as a result of the metal–salt interaction. This phase change of Cr is associated with a change in the coordination environment of Cr at the interface. DFT and AIMD simulations provide a basis for understanding the enhanced stability of δ-A15 Cr vs. bcc Cr, by revealing their competitive phase thermodynamics at elevated temperatures and probing the interfacial behavior of the molten salt at relevant facets. This study provides critical insights into the morphological and chemical evolution of metal–molten salt interfaces. Finally, the combination of multimodal synchrotron analysis and atomic simulation also offers an opportunity to explore a broader range of systems critical to energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Homogeneous ice nucleation in an ab initio machine-learning model of water

Molecular simulations have provided valuable insight into the microscopic mechanisms underlying homogeneous ice nucleation. While empirical models have been used extensively to study this phenomenon, simulations based on first-principles calculations have so far proven prohibitively expensive. Here, we circumvent this difficulty by using an efficient machine-learning model trained on density-functional theory energies and forces. We compute nucleation rates at atmospheric pressure, over a broad range of supercoolings, using the seeding technique and systems of up to hundreds of thousands of atoms simulated with ab initio accuracy. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or melting at the given supersaturation), which is used together with the equations of classical nucleation theory to compute nucleation rates. We find that nucleation rates for our model at moderate supercoolings are in good agreement with experimental measurements within the error of our calculation. We also study the impact of properties such as the thermodynamic driving force, interfacial free energy, and stacking disorder on the calculated rates.

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

Role of Oxidizing Conditions in the Dispersion of Supported Platinum Nanoparticles Explored by Ab Initio Modeling

Achieving fine control over the dispersion of supported platinum nanoparticles (Pt) is a promising avenue to enhancing their catalytic activity and selectivity. Experimental observations suggest that exposing ceria-supported Pt nanoparticles to O 2 at 500 °C promotes their dispersion into smaller particles and eventually single atoms. The exact role of oxygen in this process is not yet well understood. Past density functional theory studies of ceria-supported Pt have typically narrowed their scope to single atoms and clusters of a few atoms. Herein, we combine several approaches and types of models in a consistent atomistic framework to evaluate the relative stability of ceria-supported Pt as a function of the degree of oxidation of Pt and of the particle size, ranging from single atoms to nanoparticles 1.5 nm of diameter. Finally, we find that the largest nanoparticles remain the thermodynamically most stable species on the lowest energy facet of ceria even under oxidizing conditions, suggesting that stronger adsorption sites are required to stabilize smaller clusters and single atoms and promote oxidative dispersion.

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

An atomic cluster expansion (ACE) potential for water under extreme conditions

We present a machine learning interatomic potential for water designed to capture its complex multiphase behavior, including both molecular and superionic ice phases. The potential is based on the atomic cluster expansion (ACE) formulation and has been parameterized to enable high-fidelity molecular dynamics simulations of water under extreme conditions, for pressures up to 100 GPa and for temperatures between 500 and 6000 K. A diverse range of configurations was generated through ab initio molecular dynamics (AI-MD) simulations, covering insulating and superionic ice phases, liquid water, and dissociated plasma phase. We demonstrate that the H 2 O ACE potential accurately reproduces experimental and DFT predicted isotherms and Hugoniots. Crucially, the potential is able to capture the intricate phase behavior of water, including the transition from molecular fluid to the appropriate solid ice phases, and the superionic ice phases. This work provides a robust interatomic potential that can be used for large-scale, accurate simulations of water under extreme thermodynamic conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Role of water model on ion dissociation at ambient conditions

In this work, we study ion pair dissociation in water at ambient conditions using a combination of classical and ab initio approaches. The goal of this study is to disentangle the sources of discrepancy observed in computed potentials of mean force. In particular, we aim to understand why some models favor the stability of solvent-separated ion pairs vs contact ion pairs. We found that some observed differences can be explained by non-converged simulation parameters. However, we also unveil that for some models, small changes in the solution density can have significant effects on modifying the equilibrium balance between the two configurations. We conclude that the thermodynamic stability of contact and solvent-separated ion pairs is very sensitive to the dielectric properties of the underlying simulation model. In general, classical models are very robust in providing a similar estimation of the contact ion pair stability, while this is much more variable in density functional theory-based models. The barrier to transition from the solvent-separated to contact ion pair is fundamentally dependent on the balance between electrostatic potential energy and entropy. This reflects the importance of water intra- and inter-molecular polarizability in obtaining an accurate description of the screened ion–ion interactions.

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

First-principles molten salt phase diagrams through thermodynamic integration

Precise prediction of phase diagrams in molecular dynamics simulations is challenging due to the simultaneous need for long time and large length scales and accurate interatomic potentials. Here, we show that thermodynamic integration from low-cost force fields to neural network potentials trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid–liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange–correlation functionals for predicting the NaCl phase boundary and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid–liquid phase boundaries for any material at an ab initio level of accuracy, with the majority of the computational cost at the level of classical potentials.

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

Molecular insights into CO 2 -to-bicarbonate transformation in functionalized anion exchange ionomers for electrochemical separations

Bipolar membrane (BPM) electrochemical processes are a promising platform for carbon dioxide (CO 2 ) separations, but the molecular level thermodynamic and kinetic understanding of CO 2 -to-bicarbonate (HCO 3 − ) transformation remain poorly understood. This study employs a multiscale computational approach to systematically explore the adsorption and reactive transformation of CO 2 in five anion exchange ionomer systems. Classical molecular dynamics (MD) simulation results demonstrate that polymers with imidazolium groups significantly reduce CO 2 diffusion and enhance (OH − )–CO 2 interactions due to stronger electrostatic and π-interactions. Compared to the commonly used quaternary ammonium ionomers, imidazolium-functionalized ionomers show improved CO 2 proximity and interaction strength. Ab initio MD and density functional theory (DFT) calculations reveal that the benzyl-substituted imidazolium (IM-Ben) substantially reduces the energy barrier for HCO 3 − formation (∼72 meV lower) compared to the alkyl-substituted IM-nBu, while also mitigating imidazolium deprotonation under moderate hydration conditions. Transition state analysis shows IM-Ben forms more extensive hydrogen-bonding networks, which stabilize the transition state structure and contribute to a lower energy barrier for bicarbonate formation. These findings highlight the advantage of the adjacent benzyl moiety in enabling efficient CO 2 -to-bicarbonate transformation via hydrated hydroxide ion counterions, offering mechanistic insights and clear molecular design principles for optimizing anion exchange ionomers at bipolar membrane interfaces for electrochemical CO 2 separation applications.

Bipolar membranes, Reactive transformation of CO2,↗
DOE OSTI2023

Autonomous and dynamic precursor selection for solid-state materials synthesis

Abstract Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS 3 ) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material’s formation. Based on this information, ARROWS 3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS 3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Ab initio electronic density in solids by many-body plane-wave auxiliary-field quantum Monte Carlo calculations

We present accurate many-body results of the electronic densities in several solid materials, including Si, NaCl, and Cu. These results are obtained using the ab initio auxiliary-field quantum Monte Carlo (AFQMC) method working in a plane-wave basis with norm-conserving, multiple-projector pseudopotentials. AFQMC has been shown to be an excellent many-body total energy method. Computation of observables and correlation functions other than the ground-state energy requires back-propagation, whose adaption and implementation in the plane-wave basis AFQMC framework are discussed in the present paper. This development allows us to compute correlation functions, electronic densities, and interatomic forces, paving the way for geometry optimizations and calculations of thermodynamic properties in solids. Finite supercell size effects are considerably more subtle in the many-body framework than in independent-electron calculations. We analyze the convergence of the electronic density, and obtain best estimates for the thermodynamic limit. The densities from several typical density functionals are benchmarked against our near-exact results. The electronic densities we obtained can also be used to help construct improved density functionals.

36 MATERIALS SCIENCE↗
NASA NTRS2011

The Vaporization of B2O3(l) to B2O3(g) and B2O2(g)

The vaporization of B2O3 in a reducing environment leads to formation of both B2O3(g) and B2O2(g). While formation of B2O3(g) is well understood, many questions about the formation of B2O2(g) remain. Previous studies using B(s) + B2O3(l) have led to inconsistent thermodynamic data. In this study, it was found that after heating, B(s) and B2O3(l) appear to separate and variations in contact area likely led to the inconsistent vapor pressures of B2O2(g). To circumvent this problem, an activity of boron is fixed with a two-phase mixture of FeB and Fe2B. Both second and third law enthalpies of formation were measured for B2O2(g) and B2O3(g). From these the enthalpies of formation at 298.15 K are calculated to be -479.9 +/- 41.5 kJ/mol for B2O2(g) and -833.4 +/- 13.1 kJ/mol for B2O3(g). Ab initio calculations to determine the enthalpies of formation of B2O2(g) and B2O3(g) were conducted using the W1BD composite method and show good agreement with the experimental values.

Jacobson, Nathan S.↗