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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Computational Framework to Accelerate the Discovery of Perovskites for Solar Thermochemical Hydrogen Production: Identification of Gd Perovskite Oxide Redox Mediators

A high-throughput computational framework to identify novel multinary perovskite redox mediators is presented, and this framework is applied to discover the Gd-containing perovskite oxide compositions Gd 2 BB'O 6 , GdA'B 2 O 6 , and GdA'BB'O 6 that split water. The computational scheme uses a sequence of empirical approaches to evaluate the stabilities, electronic properties, and oxygen vacancy thermodynamics of these materials, including contributions to the enthalpies and entropies of reduction, ΔH TR and ΔS TR . This scheme uses the machine-learned descriptor τ to identify compositions that are likely stable as perovskites, the bond valence method to estimate the magnitude and phase of BO 6 octahedral tilting and provide accurate initial estimates of perovskite geometries, and density functional theory including magnetic- and defect-sampling to predict STCH-relevant properties. Eighty-three promising STCH candidate perovskite oxides down-selected from 4392 Gd-containing compositions are reported, three of which are referred to experimental collaborators for characterization and exhibit STCH activity. Our results demonstrate that the high-throughput computational scheme described herein—which is used to evaluate Gd-containing compositions but can be applied to any multinary perovskite oxide compositional space(s) of interest—accelerates the discovery of novel STCH active redox mediators with reasonable computational expense.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Pd–Methyl Bond Energy─Property Correlations, Noncorrelations, Machine Learning Models, and Application to Polymerization Catalysis

Metal–carbon bonds are a key intermediate in a variety of homogeneous organometallic transformations and often determine the critical thermodynamics and kinetics of catalytic processes. Surprisingly, the influence of different ligands on metal–carbon bond strengths has been largely overlooked. Here, in this study, we evaluated nearly 700 experimental Pd–methyl complexes by calculating their bond dissociation energies using density functional theory (DFT) and compared these bond strengths to several fundamental molecular properties, and this revealed several surprising correlations and noncorrelations. Most surprising was that several fundamental properties, such as the bond length, bond force constant, and bond electron density, have no correlation with bond strength, despite these correlations often holding for main-group compounds. We were indeed able to identify key ligand-dependent chemical features/descriptors that provided a highly accurate machine learning model and provided insight into the general factors that control the Pd–carbon bond strength, such as radical delocalization and nucleophilicity. Insights gained from the Pd–Me bond energy analysis were then applied to CO migratory insertion steps that are part of copolymerization reactions.

binding energy↗
DOE OSTI2025

Gold-Standard Chemical Database 137 (GSCDB137): A Diverse Set of Accurate Energy Differences for Assessing and Developing Density Functionals

We present GSCDB137, a rigorously curated benchmark library of 137 data sets (8377 entries) covering main-group and transition-metal reaction energies and barrier heights, (intra- and intermolecular) noncovalent interactions, dipole moments, polarizabilities, electric-field response energies, and vibrational frequencies. Legacy data from GMTKN55 and MGCDB84 have been updated to today's best reference values; redundant or low-quality points were removed, and many new, property-focused sets were added. Testing 29 popular density functional approximations (DFAs) confirms the expected Jacob's-ladder hierarchy overall but also reveals notable exceptions: functional performance for frequencies and electric-field properties correlates poorly with that for other ground-state energetics. ωB97M-V and ωB97X-V are the most balanced hybrid meta-GGA and hybrid GGA, respectively; B97M-V and revPBE-D4 lead the meta-GGA and GGA classes. Double hybrids lower mean errors by about 30% versus their hybrid analogues but demand careful frozen-core, basis set, and spin contamination treatment. GSCDB137 offers a comprehensive, openly documented platform for rigorous validation of DFA and universal machine learning potentials, and training of the next generation of exchange-correlation functionals.

Liang, Jiashu [University of California, Berkeley,↗
DOE OSTI2020

Spin Splitting Energy of Transition Metals: A New, More Affordable Wave Function Benchmark Method and Its Use to Test Density Functional Theory

Accurately predicting the spin splitting energy of chemical species is important for understanding their reactivity and magnetic properties, but it is very challenging, especially for molecules containing transition metals. One impediment to progress is the scarcity of accurate benchmark data. Here we report a set of calculations designed to yield reliable benchmarks for simple transition-metal complexes that can be used to test density functional methods that are affordable for large systems of more practical interest. Various wave function methods are tested against experiment for Fe 2+ , Fe 3+ , and Co 3+ , including CASSCF, CASPT2, CASPT3, MRCISD, MRCISD+Q, ACPF, AQCC, CCSD(T), and CASPT2/CCSD(T) and also a new method called CASPT2.5, which is performed by taking the average of the CASPT2 and CASPT3 energies. Furthermore, we find that MRCISD+Q, ACPF, and AQCC require smaller active spaces for good accuracy than are required by CASPT2 and CASPT3, and this aspect may be important for calculations on larger molecules; here we find that CASPT2.5 extrapolated to a complete basis set is the most suitable method—in terms of computational cost and in terms of accuracy on monatomic systems—and therefore we chose this method for molecular benchmarks. Then Kohn–Sham density functional calculations with 60 exchange-correlation functionals are tested for FeF 2 , FeCl 2 , and CoF 2 . We find that MN15-L, M06-SX, and revM06 have very good agreement with CASPT2.5 benchmarks in terms of both the spin splitting energy and the optimized geometry for each spin state. In addition, we recommend def2-TZVP as the most suitable basis set to perform density functional calculations for molecular spin splitting energies; extra polarization functions in the basis set do not help to increase the accuracy of the spin splitting energy in KS calculations.

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

First-Principles-Based Study of the Decomposition of Phenol and Hydroquinone on Pt(111) Combined with Quantitative Information from XPS Spectra to Address the Impact of Coverage and Number of Hydroxyl Functional Groups

A combined first-principles-based and experimental X-ray photoelectron spectroscopy approach was used to investigate the thermal decomposition of two model biofuel compounds, phenol and hydroquinone, on Pt(111) at both low and high coverages. The DFT-based approach yields adsorption geometries and energies, activation barriers and core-level binding energy shifts for C 1s and O 1s. Increasing the coverage in the theoretical model leads to slight shifts in core-level binding energies─toward higher values for C 1s and lower values for O 1s. It also alters the energy profiles of the decomposition reaction pathway, resulting in weaker adsorption energies and changes in both reaction and activation barriers. At low temperatures, we observe a multilayer for phenol and hydroquinone upon adsorption, with desorption occurring at 200 and 270 K, respectively. Following desorption of the multilayer, decomposition proceeds via initial O–H bond scission, followed by two parallel pathways involving either C–H or C–C bond scission, whereby in the case of phenol C–H bond scission occurs first. Here, we further provide characteristic core level binding energies by theoretical calculations that are subsequently used in experimental analyses, establishing a reference database for key spectra of phenolic functionalities applicable to a range of catalytic reactions.

09 BIOMASS FUELS↗
DOE OSTI2025

Deep potential molecular dynamics simulations of ion-enhanced etching of silicon by atomic chlorine

The continued development of plasma-assisted processing techniques requires a fundamental understanding of plasma-surface interactions. Molecular dynamics (MD) simulations have been employed to complement experimental studies and better understand the properties of such systems. Recently, machine learning (ML) methods have enabled the development of ab initio-based interatomic potentials, which can be generalized to complex combinations of multiple atom types. In this work, we use ML potentials developed using the Deep Potential Molecular Dynamics (DeepMD) framework to provide a model of ion-enhanced etching of Si by Cl atoms. We demonstrate the importance of proper selection of the training data set to the accuracy of the DeepMD model and compare our results to MD results using empirical potentials, as well as to experimental measurements. Exposure of undoped Si at 300 K to thermal Cl atoms yields a steady-state Cl coverage of 1.25 monolayers, which is slightly lower than the value obtained in previous experimental studies. Predictions of Si etch yields by simultaneous Cl atom and Ar + ion impacts as a function of ion energy, neutral to ion flux ratio, and angle of incidence of the ions are in reasonably good agreement with classical MD results and experimental measurements. Finally, etch yields and SiCl x mixed layer thicknesses during simultaneous bombardment of the Si(100) surface by Cl atoms and Cl + ions are in good agreement with experimental data. In conclusion, the present work is a necessary condition for the extension of the DeepMD procedure to more complex systems of interest in plasma-surface interactions.

Artificial neural networks↗
DOE OSTI2023

Revealing quasi-excitations in the low-density homogeneous electron gas with model exchange–correlation kernels

Time-dependent density functional theory within the linear response regime provides a solid mathematical framework to capture excitations. The accuracy of the theory, however, largely depends on the approximations for the exchange–correlation (xc) kernels. Away from the long-wavelength (or q = 0 short wave-vector) and zero-frequency (ω = 0) limit, the correlation contribution to the kernel becomes more relevant and dominant over exchange. The dielectric function, in principle, can encompass xc effects relevant to describe low-density physics. Furthermore, besides collective plasmon excitations, the dielectric function can reveal collective electron–hole excitations, often dubbed “ghost excitons.” Besides collective excitons, the physics of the low-density regime is rich, as exemplified by a static charge-density wave that was recently found for r s > 69, and was shown to be associated with softening of the plasmon mode. These excitations are seen to be present in much higher density 2D homogeneous electron gases of r s ≳ 4. Here, in this work, we perform a thorough analysis with xc model kernels for excitations of various nature. The uniform electron gas, as a useful model of real metallic systems, is used as a platform for our analysis. We highlight the relevance of exact constraints as we display and explain screening and excitations in the low-density region.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations

Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present, they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new machine learning approach called "BpopNN" for obtaining efficient approximations to DFT PESs. Conceptually, the methodology is based on approaching the true DFT energy as a function of electron populations on atoms; in practice, this is realized with available density functionals and constrained DFT (CDFT). Additionally, the new approach creates approximations to this function with neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on LinHn clusters.

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

The role of precursor decomposition in the formation of samarium doped ceria nanoparticles via solid-state microwave synthesis

The impact on the final morphology of ceria (CeO 2 ) nanoparticles made from different precursors (commercial: cerium acetate/nitrate) and in house: cerium tri(methylsilyl)amide (Ce-TMSA)) via a microwave solid state reaction has been determined. In all instances, powder X-ray diffraction indicated that the cubic fluorite CeO 2 phase (PDF# 04–004-9150, with the space group Fm-3 m) had formed. Scanning electron microscopy (SEM) images revealed spherical nanoparticles were produced from the Ce-TMSA precursor. The commercial acetate and nitrate precursors produced particles with irregular morphology. The roles of the precursor decomposition and binding energy in the synthesis of the nanocrystals with various morphologies, as well as a possible growth mechanism, were evaluated based on experimental and computational data. The formation of spherical shaped nanoparticles was determined to be due to the preferential single-step decomposition of the Ce-TMSA as well as the low activation energy to overcome decomposition. Due to the complicated decomposition of the commercial precursors and high activation energy the resulting particles adopted an irregular morphology. Highly uniform samarium doped ceria (Sm x Ce 1-x O 2-δ ) nanospheres were also synthesized from Ce-TMSA and samarium tri(methylsilyl)amide (Sm-TMSA). The effects of reaction time and temperature, on the final morphology were observed through SEM. The rapid single-step decomposition of TMSA-based precursors as observed through thermogravimetric analysis (TGA) and confirmed through the calculation of potential energy surfaces and binding energies from density functional theory (DFT) calculations, indicated that nanoparticle formation follows LaMer’s classical nucleation theory.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Kinetic model describing self-limiting CO 2 diffusion in supported amine adsorbents

A reaction–diffusion shrinking core model describing the decay in diffusivity of supported amine sorbents upon CO 2 sorption under both simulated direct air capture and point source capture conditions is described. The decay in CO 2 diffusivity is associated with crosslinking in the aminopolymer samples and general pore blockage in the amino-silane derived samples, which occurs as CO 2 is adsorbed. The model is used to extract four kinetic parameters that govern the CO 2 uptake kinetics and working capacity: an apparent reaction rate constant, an initial effective diffusivity, and two dimensionless decay parameters. Ideally, an initially reaction limited system would allow for direct determination of the intrinsic reaction rate constant; however, sorption experiments suggest mass transfer resistances related to gas mixing, external boundary layers and intraparticle diffusion are present. Reaction rate constants are determined and agree well with theoretical values predicted with the Eyring equation parameterized using density functional theory energies from literature sources. The kinetic performance is expressed as the average effective diffusivity as a function of average conversion, which can be correlated to the dispersion of sorption sites on the support and the morphology of the active sorbent phase. Four supports are impregnated or grafted with amines, SBA-15, single-walled zeolite nanotubes (ZNT), Syloid SiO 2 , and γ-Al 2 O 3 . Due to its pore structure, γ-Al 2 O 3 supported amines can be modeled at the μm scale or at the nm scale, where the shell balance is on the μm-sized macroporous particle aggregate or on the nm-sized amine film on the surface of the Al 2 O 3 nanoparticles, which comprise the spherical particle aggregates. Faster diffusion rates are maintained under 400 ppm rather than 10% CO 2 due to a slower reaction rate giving a slower decay in diffusivity. In conclusion, this work provides a first principles kinetic analysis of CO 2 sorption where previous models are semi-empirical and use arbitrary kinetic parameters.

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

Isoscalar and isovector giant resonances in 44 Ca, 54 Fe, 64,68 Zn and 56,58,60,68 Ni

We have studied the uncharacteristic behavior of the measured values of the isoscalar and isovector centroid energies, E CEN , of nuclear giant resonances of multipolarity from L = 0 to L = 3 in 44 Ca, 54 Fe, 64,68 Zn and 56,58,60,68 Ni. For this purpose, we carried out calculations of E CEN within the spherical Hartree-Fock (HF)-based random phase approximation (RPA) theory with 33 distinct Skyrme-like effective nucleon-nucleon interactions. We have also determined the Pearson linear correlation coefficients between centroid energies, obtained from the HF-RPA, and the various properties of nuclear matter (NM) of each interaction and determined the sensitivity of E CEN to NM properties. We compared the theoretical values of E CEN obtained from the HF-RPA calculations with experimental data and discuss the results, pointing out significant disagreements between theoretical and experimental values. We note in particular, that we obtain good agreement for the theoretical E CEN of the isovector giant dipole resonance and the available experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Theory of nuclear fission

Atomic nuclei are quantum many-body systems of protons and neutrons held together by strong nuclear forces. Under the proper conditions, nuclei can break into two (sometimes three) fragments which will subsequently decay by emitting particles. This phenomenon is called nuclear fission. Since different fission events may produce different fragmentations, the end-products of all fissions that occurred in a small chemical sample of matter comprise hundreds of different isotopes, including α particles, together with a large number of emitted neutrons, photons, electrons and antineutrinos. The extraordinary complexity of this process, which happens at length scales of the order of a femtometer, mostly takes less than a femtosecond but is not entirely over until all the lingering β decays have completed – which can take years – is a fascinating window into the physics of atomic nuclei. While fission may be more naturally known in the context of its technological applications, it also plays a crucial role in the synthesis of heavy elements in astrophysical environments. In both cases, simulations are needed for the many systems or energies inaccessible to experiments in the laboratory. In this context, the level of accuracy and precision required poses formidable challenges to nuclear theory. Overall, the goal of this article is to provide a comprehensive overview of the theoretical methods employed in the description of nuclear fission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Enumeration as a Tool for Structure Solution: A Materials Genomic Approach to Solving the Cation-Ordered Structure of Na 3 V 2 (PO 4 ) 2 F 3

While powder diffraction methods are routinely utilized to optimize structural models for compounds whose crystal structures are known, the determination of unknown structures is far more challenging. When the unknown structure is large, structure solution can become a virtually intractable problem using standard structure solution methodologies, especially when the space group cannot be unambiguously resolved. One such system is the promising Na-ion battery cathode material Na 3 V 2 (PO 4 ) 2 F 3 whose high temperature and room temperature structures were previously solved, but whose more complex low-temperature structure could not be determined. Here, a novel materials genomic approach is demonstrated for the solution of the unknown 100 K structure of Na 3 V 2 (PO 4 ) 2 F 3 in which enumeration methods are first used to generate a large number (~3,000) of trial structures based on plausible orderings of Na ions and then automated Rietveld refinements are carried out to optimize each of these trial structures. Based on both the analysis of the ensemble of optimized trial structures and the density functional theory energy minimization of selected trial structures, the 100 K structure of Na 3 V 2 (PO 4 ) 2 F 3 is best described as belonging to the space group A2 1 am with unit cell dimensions of a = 9.01928(4), b = 27.1379(1), c = 10.73307(5). The 100 K unit cell has a large volume of 2627.07(2) Å 3 with Z = 12 and 33 independent crystallographic sites (9 Na, 3 V, 3 P, 12 O, and 6 F) that is 3x and 6x larger than the room- and high-temperature polymorphs of this phase, respectively. Finally, the novel methods described here will be generally applicable for the solution of the complex cation-ordered structures that commonly occur for battery materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations

The encapsulation and commoditization of electronic structure arise naturally as interoperability, and the use of nontraditional compute resources (e.g., new hardware accelerators, cloud computing) remains important for the computational chemistry community. Here, we present TERACHEM CLOUD, a high-performance computing service (HPCS) that offers on-demand electronic structure calculations on both traditional HPC clusters and cloud-based hardware. The framework is designed using off-the-shelf web technologies and containerization to be extremely scalable and portable. Within the HPCS model, users can quickly develop new methods and algorithms in an interactive environment on their laptop while allowing TERACHEM CLOUD to distribute ab initio calculations across all available resources. This approach greatly increases the accessibility of hardware accelerators such as graphics processing units (GPUs) and flexibility for the development of new methods as additional electronic structure packages are integrated into the framework as alternative backends. Cost-performance analysis indicates that traditional nodes are the most cost-effective long-term solution, but commercial cloud providers offer cutting-edge hardware with competitive rates for short-term large-scale calculations. We demonstrate the power of the TERACHEM CLOUD framework by carrying out several showcase calculations, including the generation of 300,000 density functional theory energy and gradient evaluations on medium-sized organic molecules and reproducing 300 fs of nonadiabatic dynamics on the B800-B850 antenna complex in LH2, with the latter demonstration using over 50 Tesla V100 GPUs in a commercial cloud environment in 8 h for approximately $1250.

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

Modulating coordinate site occupancy in high-entropy spinel electrocatalysts

High entropy spinel oxides provide a versatile platform for electrocatalysis because multiple metal cations can be incorporated into a single crystalline lattice, enabling tunable electronic structures. However, controlling how these cations distribute between tetrahedral and octahedral coordination sites remains a major challenge, limiting rational catalyst design. Here, we modulate cation coordination site occupancy between tetrahedral and octahedral sites in a Co–Fe–Cr–Mn–Ni framework by introducing a sixth cation (Zn, Ga, Mg, or Al) with distinct site preference energies. Using density functional theory, synchrotron X-ray absorption spectroscopy, and magnetic circular dichroism, we demonstrate that Zn preferentially occupies tetrahedral sites, driving increased octahedral occupancy of cobalt. This redistribution increases the population of octahedrally coordinated cobalt in mixed oxidation states, enhances electrical conductivity, and improves oxygen evolution reaction activity. Our findings establish coordination site occupancy as a critical design parameter, providing a strategic pathway for tailoring multicomponent spinel electrocatalysts with optimized performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗