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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Nickel B-site substitution in bulk Sr 1-x Ca x FeO 3 perovskite oxygen carriers: Benefits and limitations

We report oxygen (O 2 ) storage materials often rely on the presence of cobalt (Co) to reduce the thermodynamic penalty and increase the kinetics necessary for efficient O 2 storage and release. In this work, we investigate nickel (Ni) as an alternative B-site dopant in Sr 1-x Ca x FeO 3 to identify Co-free carriers that still show improved kinetics at low temperatures. In fact, we show a substantial increase in the reversible O2 release rate through mild Ni B-site substitution (y = 0.06) in select Sr 1-x Ca x Fe 1-y Ni y O 3 systems at 400 to 500 °C, reaching 2.00 wt.% O 2 release up to approximately 75% faster than Ni-free systems. To explain the role of Ni in these systems, we use density functional theory to calculate the O 2 vacancy (V O ) formation energy from separate metal-oxygen (M-O) bonding and relaxation components. We computationally show elongated Ni-O bonds are directly responsible for the decrease in V O upon Ni substitution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

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

An Accurate, Affordable Density Functional Tight-Binding Model for Excited State Hydrocarbon Polymer Molecular Dynamics

We have developed a density functional tight-binding model for hydrocarbon excited state dynamics by referencing to high-level electronic structure theory and incorporating a many-body repulsive energy. We then validate our model against n-octane geometry optimizations, bond dissociation scans, and vibrational frequencies. Our model is approximately 10 3 times more efficient than hybrid time-dependent density functional theory calculations with comparable accuracy. In conclusion, our efforts enable longer timescale excited state simulations of photochemistry and scattering of incident radiation.

Chemical calculations↗
DOE OSTI2021

Accurate Thermochemistry of Complex Lignin Structures via Density Functional Theory, Group Additivity, and Machine Learning

A molecular-level understanding of lignin structures and bond dissociation energies could facilitate depolymerization technologies. Still, this information is currently limited due to the lack of databases and the simplification of surrogate models. Here, substitution effects on seven common linkages in lignin polymers are systematically investigated. An automated reaction network generator is employed to create a database of structures. A new group additivity (GA) model based on principal component analysis (PCA) descriptors is introduced and trained on gas-phase density functional theory data of 4100 species at the M06-2X/6-311++G(d,p) level. Hydrogen bonds, local steric, and nonaromatic ring contributions are also incorporated. Lastly, we improve the accuracy of the group additivity model to reach the G4 theory by computing a data set of 770 species at this level and using a data fusion approach.

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

A non-perturbative pairwise-additive analysis of charge transfer contributions to intermolecular interaction energies

Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals (ALMOs) decomposes the interaction energy between molecules into physically interpretable components like geometry distortion, frozen interactions, polarization, and charge transfer (CT, also sometimes called charge delocalization) interactions. In this work, a numerically exact scheme to decompose the CT interaction energy into pairwise additive terms is introduced for the ALMO-EDA using density functional theory. Unlike perturbative pairwise charge-decomposition analysis, the new approach does not break down for strongly interacting systems, or show significant exchange–correlation functional dependence in the decomposed energy components. Both the energy lowering and the charge flow associated with CT can be decomposed. Complementary occupied–virtual orbital pairs (COVPs) that capture the dominant donor and acceptor CT orbitals are obtained for the new decomposition. It is applied to systems with different types of interactions including DNA base-pairs, borane-ammonia adducts, and transition metal hexacarbonyls. While consistent with most existing understanding of the nature of CT in these systems, we report the results also reveal some new insights into the origin of trends in donor–acceptor interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Density-Functional Tight-Binding Parameters for Bulk Zirconium: A Case Study for Repulsive Potentials

Density-functional tight-binding (DFTB) parameters are presented for the simulation of the bulk phases of zirconium. Electronic parameters were obtained using a band structure fitting strategy, while two-center repulsive potentials were created by particle swarm optimization. As objective functions for the repulsive potential fitting, we employed the Birch–Murnaghan equations of state for hexagonal close-packed (HCP), body-centered cubic (BCC) and ω phases of Zr from density-functional theory (DFT). When fractional atomic coordinates are not allowed to change in the generation of the equation-of-state curves, long-range repulsive DFTB potentials are able to almost perfectly reproduce equilibrium structures, relative DFT energies of the bulk phases, and bulk moduli. However, the same potentials lead to artifacts in the DFTB potential energy surfaces when atom positions in the unit cell are allowed to fully relax during the change of unit cell parameters. Finally, conventional short-range repulsive DFTB potentials, while inferior in their ability to reproduce DFT bulk energetics, are able to correctly reproduce the qualitative shape of the DFT potential energy surfaces, including the location of global minima, and can therefore be considered more transferable.

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

Targeted Chemical Looping Materials Discovery by an Inverse Design

Chemical looping with oxygen uncoupling (CLOU) materials is actively sought for combustion of carbonaceous materials to achieve complete conversion and capture of carbon dioxide. These materials may play a vital role in reducing atmospheric carbon via negative carbon output. However, there is no one‐size‐fits‐all approach as different operating conditions and feedstocks may require different CLOU materials. As a result, the exploration and discovery of high‐performance CLOU materials can be a slow process. To address this challenge, a high‐throughput inverse machine learning workflow that identifies optimum materials from perovskite oxides for a given set of targets is developed—temperature and Gibbs free energy of oxygen formation. The model is trained on high‐throughput density functional theory calculations of CLOU materials and inverts the materials design process using a genetic algorithm to produce realistic substituted SrFeO 3‐δ compositions as output. Using the inverse model, it is able to identify several interesting new families of CLOU materials: Sr 1‐ x A x Fe 1‐ y B y O 3‐δ (e.g., A = Ca or K; B = Mg, Bi, Mn, Ni, Co, Cu, or Zn). These materials have shown promising properties, and some of them even outperform the benchmark material in terms of oxygen release kinetics under relevant CLOU operating conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deformation Behaviors in Single BCC‐Phase Refractory Multi‐Principal Element Alloys under Dynamic Conditions

The mechanical behavior and microstructural evolution of a BCC-phase NbTaTiV refractory multi-principal element alloy (RMPEA) is studied over a wide range of strain rates (10 −3 to 10 3 s −1 ) and temperatures (room temperature to 850 °C). The mechanical property of present RMPEA shows less strain-rate dependence and strong resistance to softening at high temperatures. Under high strain-rate loading, the formation of thin type-I twins is observed, which could lead to an increase in strain-hardening rates. However, this hardening mechanism competes with adiabatic heating effects, resulting in the deterrence of strain-hardening behaviors. In contrast, substantial strain-hardening occurs at cryogenic temperatures due to the formation of twins, which act as stronger barriers to dislocation motion and interact with each other. To further understand the different strain-hardening behaviors, density functional theory (DFT) calculations predict relatively low stacking fault energies and high twinning stress for the NbTaTiV RMPEA.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dissecting Critical Factors for Electrochemical CO 2 Reduction on Atomically Precise Au Nanoclusters

Here, this work investigates the critical factors impacting electrochemical CO 2 reduction reaction (CO 2 RR) using atomically precise Au nanoclusters (NCs) as electrocatalysts. First, the influence of size on CO 2 RR is studied by precisely controlling NC size in the 1–2.5 nm regime. We find that the electrocatalytic CO partial current density increases for smaller NCs, but the CO Faradaic efficiency (FE) is not directly associated with the NC size. This indicates that the surface-to-volume ratio, i.e. the population of active sites, is the dominant factor for determining the catalytic activity, but the selectivity is not directly impacted by size. Second, we compare the CO 2 RR performance of Au 38 isomers (Au 38 Q and Au 38 T) to reveal that structural rearrangement of identical size NCs can lead to significant changes in both CO 2 RR activity and selectivity. Au 38 Q shows higher activity and selectivity towards CO than Au 38 T, and density functional theory (DFT) calculations reveal that the average formation energy of the key *COOH intermediate on the proposed active sites is significantly lower on Au 38 Q than Au 38 T. These results demonstrate how the structural isomerism can impact stabilization of reaction intermediates as well as the overall CO 2 RR performance of identical size Au NCs. Overall, this work provides important structure–property relationships for tailoring the NCs for CO 2 RR.

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

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗
DOE OSTI2026

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗
DOE OSTI2020

Extend NdJ relationship with the size, multiple exchanges and Dzyaloshinskii-Moriya interaction for Néel skyrmions in hexagonal magnetic interfaces

For the integration with other parts of spintronic devices, it is desired to minimize the size of skyrmions to a nanometer scale. To provide a theoretical guidance for this endeavour, we established an extended NdJ relationship between the skyrmion size and fundamental magnetic properties of two-dimensional hexagonal ferromagnetic lattices, including multiple exchanges between different neighbors (J 1 -J 4 ), Dzyaloshinskii-Moriya interactions (d 1 and d 2 ) and on-site magnetic anisotropy energy. We demonstrated the applicability of this approach through density functional theory and spin spiral calculations for several interface, e.g., Co/Gra, Rh/Gra, Co/Pt 3 and MgO/Co/Pt 3 , and showed that the size of Néel skyrmions can be effectively reduced by either enhancing d or more conveniently reducing J through cancelation of J i s.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modified microstructures in proton irradiated dual phase 308L weldment filler material

In this study, the effect of proton irradiation on the microstructure of δ ferrite—γ austenite mixed phase 308L filler material in a 508–304 dissimilar metal weldment was investigated over a depth of 0 to 10 µm. Ni–Si–Mn G-phase precipitates were observed with SEM and TEM in δ ferrite but not in γ austenite. Our density functional theory based calculations show that the G/Fe interface energy in δ-Fe is significantly lower than that in γ-Fe (0.35 versus 1.25 J/m 2 ), which provides a thermodynamics-based explanation for our experimental observations of preferential formation of G-phase in δ ferrite. STEM-EDS, TEM dark field imaging, and diffraction patterns confirmed the Ni–Si–Mn enriched precipitates were G-phase precipitates with a stoichiometry of Mn 6 Ni 16 Si 7 . Intragranular voids and Ni–Si enriched clusters were observed in irradiated γ austenite. Additionally, Ni and Si segregation was observed along the void interfaces. In both cases, Ni–Si clusters and segregation to voids, selected area diffraction patterns did not reveal the existence of a second phase. Proton irradiation induced Cr depletion and Si and Ni enrichment at γ-γ austenite grain boundaries that was characterized with STEM/EDS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Level density within a micro-macroscopic approach

Statistical level density $ρ$($E, A$) is derived for nucleonic system with a given energy $E$, particle number $A$ and other integrals of motion in the micro-macroscopic approximation beyond the standard saddle-point method of the Fermi gas model. This level density reaches the two limits; the well-known Fermi gas grand-canonical ensemble limit for a large entropy $S$ related to large excitation energies, and the finite micro-canonical limit for a small combinatorical entropy $S$ at low excitation energies. In conclusion, the inverse level density parameter $K$ as function of the particle number $A$ in the semiclassical periodic orbit theory, taking into account the extended Thomas-Fermi and Strutinsky shell corrections, is calculated and compared with experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Determining the Structure-Property Relationships of Quasi-Two-Dimensional Semiconductor Nanoplatelets.

We report a theoretical study of CdSe nanoplatelets aimed at identifying the main factors determining their photophysical properties. Using atomic configurations optimized with density functional theory calculations, we computed quasiparticle and exciton binding energies of nanoplatelets with two to seven monolayers. We employed many body perturbation theory at the GW level and solved the Bethe-Salpeter equation to obtain absorption spectra and excitonic properties. Our results, which agree well with recent experiments, were then used to design a model that allows us to disentangle the effects of quantum confinement, strain induced by passivating ligands, and dielectric environment on the electronic properties of nanoplatelets. We found that, for the model to accurately reproduce our first principle results, it is critical to account for surface stress and consider a finite potential barrier and energy-dependent effective masses when describing quantum confinement. Our findings call into question previous assumptions on the validity of an infinite barrier to describe carrier confinement in nanoplatelets, suggesting that it may be possible to optimize interfacial charge transfer and extraction by appropriately choosing passivating ligands. The model developed here is generalizable to core-shell platelets and enables the description of system sizes not yet directly treatable by first-principles calculations.

Greenwood, Arin R.↗
DOE OSTI2023

Impact of Disorder and Defects on Bond Energies in Highly Fluorinated Graphene

Fluorinated graphite materials are of interest for an assortment of applications and can be synthesized under a variety of synthetic conditions from many different types of carbon. Due to such variations, structural disorders in the form of defects and polymorphism are often present. Here, we investigate the impact of local structural variations on the C–F bond dissociation energies (BDEs) in carbon-based fluoride materials using density functional theory (DFT) computational methods. Employing fluorographene (FG) cluster models, we determine the impact of different C–F bonding configurations in the core of each platelet on the equilibrium BDEs for each C–F bond. The introduction of structural disorder decreases the first C–F BDE by approximately 1 eV compared to the canonical arrangement of axial C–F bonds ordered as in a network of cyclohexane “chairs”. Variability of calculated BDEs among the different polymorphs decreases upon subsequent F removal. Common structural tendencies of the adiabatic defluorination pathways for each polymorph are identified. In conclusion, our analysis suggests that at F/C ratios near 1.0, disorder in the local structure can play a significant role in the energetics of the initial carbon fluoride defluorination and that the influence of this configurational disorder diminishes with decreasing F/C ratios.

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

The Two-Energies Turbulence Scheme Coupled to the Assumed PDF Method

An update of the two-energy turbulence scheme is presented, the 2TE + APDF scheme. The original version of the two-energy scheme is able to successfully model shallow convection without the need of an additional parameterization for non-local fluxes. However, the performance of the two-energy scheme is worse in stratocumulus cases, where it tends to overestimate the erosion of the stable layers. We have identified the causes: the non-local stability parameter does not consider local stratification, the scheme lacks an internal parameter that could distinguish between a shallow convection regime and a stratocumulus regime, and it uses an inflexible turbulence length scale formulation. To alleviate this problem, we propose several modifications: an update of the stability parameter, a modified computation of the turbulence length scale, and the introduction of the entropy potential temperature to distinguish between a shallow convection and a stratocumulus regime. In addition, the two-energy scheme is coupled to a simplified assumed probability density function method in order to achieve a more universal representation of the cloudy regimes. The updated turbulence scheme is evaluated for several idealized cases and one selected real case in the ICOsahedral Nonhydrostatic (ICON) modeling framework. The results show that the updated scheme corrects the overmixing problem in the stratocumulus cases. The performance of the updated scheme is comparable to the operational setup, and can be thus used instead of the operational turbulence and shallow convection scheme in ICON. Additionally, the updated scheme improves the coupling with dynamics, which is beneficial for the modeling of coherent flow structures in the atmospheric boundary layer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Testing the predictive power of theory for Pd x Ir (100-x) alloy nanoparticles for the oxygen reduction reaction

Here, density functional theory (DFT) calculations of O and OH binding energies on triatomic surface ensembles of Pd x Ir (100-x) nanoalloys successfully predicted the overall trend in experimental oxygen reduction reaction (ORR) activity as a function of nanoparticle (NP) composition. Specifically, triatomic Pd 3 ensembles were found to possess optimal O and OH binding energies and were predicted to be highly active sites for the ORR, rivaling that of Pt(111). However, DFT calculations suggest that the O binding energy increases at active sites containing Ir, thereby decreasing ORR activity. Pd x Ir (100-x) nanoalloys were synthesized using a microwave-assisted method and their activity towards the ORR was tested using rotating disk voltammetry (RDV). As predicted, the bimetallic electrocatalysts exhibited worse catalytic activity than the Pd-only NPs. The strong qualitative correlation between the theoretical and experimental results demonstrates that the activity of individual active sites on the surface of NPs can serve as a proxy for overall activity. This is a particularly useful strategy for applying DFT calculations to electrocatalysts that are too large for true first-principle analysis.

36 MATERIALS SCIENCE↗